Profit Of Platform Research Articles

A dynamic analysis of a green closed-loop supply chain with different on-line platform smart recycling and selling models

Global warming and waste product recycling have become increasingly serious. Green innovation of a closed-loop supply chain (GCLSC) and selection of on-line platform recycling and sales models need to be solved as a matter of urgency. To achieve this purpose, dynamic influences of green innovation, platform smart marketing, and recycling investments on goodwill are considered. Dynamic game models are used to solve the equilibrium solutions and optimal trajectories under different models, and influences of commission rates on the profits and key variables are compared via numerical simulations. The results show that the evolution paths of goodwill under the four decision models all descend at first, then stabilize; the optimal strategy can be selected based on the intervals of sales and recycling commission rates. Under the dual-agency model (agency selling and platform agency recycling (Model AP)), the higher the sales commission rate (SCR) is, the more likely the manufacturer chooses the reselling and platform agency recycling (Model RP), which, however, may lead to insufficient green innovation investment. The higher the RCR, the greater the platform profit when selecting agency selling. The system profit first increases, then decreases with growing sales commission, and Model AP represents the optimal strategy. The research offers reference for the selection of operation models and investment decision, is conducive to improving the enterprise goodwill, and promotes the sustainable development of the GCLSC.

Modelling the impacts of en-route ride-pooling service in a mixed pooling and non-pooling market

En-route ride-pooling services, e.g. Didi Pinche, UberPool, have been considered a promising way to alleviate traffic congestion during rush hours. Yet in reality, its market share is still small compared to non-pooling service. For a ride-hailing platform that provides both en-route pooling and non-pooling services, this paper endeavors to answer under what circumstance would it have incentive to encourage ride-pooling, and whether drivers, riders, the platform and the society can simultaneously benefit from the introduction of en-route ride-pooling service. We propose an aggregate model to characterize the equilibrium of a ride-hailing market where a platform operates a fleet of vehicles to provide en-route pooling and non-pooling services. The model captures the complex interactions among riders’ mode choices between pooling and non-pooling, the waiting times of pooling and non-pooling users, the pairing probability and the expected detour time of pooling users, the matching rates of cruising and halfly occupied vehicles with different types of riders, and the numbers of cruising, halfly occupied and fully occupied vehicles at each instant. Based on the model, we theoretically reveal the impacts of the total ride-hailing demand, vehicle fleet size, and the platform’s pricing and vehicle dispatching strategies on the market equilibrium performance through partial derivative-based sensitivity analysis, and establish conditions under which the introduction of en-route ride-pooling service improves platform profit, driver income, rider utility and social welfare.

The value of blockchain for shipping platforms: a perspective from multi-homing and network effects

Traditional information structures within shipping supply chain platforms often grapple with inefficient decision-making processes, hindering overall efficiency. Blockchain is increasingly utilised in shipping platforms, employing decentralised distributed ledgers to transform the information architecture of supply chains, which are typically characterised by centralised management and fragmented data. However, the ceasing of operations such as TradeLens and 300cubits due to high operating costs, user privacy concerns, and multiple bets also makes investing in blockchain an obscure choice. Motivated by this dilemma about the value of blockchain to platforms, we investigate the impact of user behaviour on blockchain investments in shipping platforms through a game-theoretic model of heterogeneous platform competition. First, our results show that the relationship between the intrinsic value and the option value of blockchain is a crucial determinant of its applicability to platforms. Second, we establish that, although the network effects enhance platform profits, higher network effects exacerbate price competition and hinder the value of the blockchain. Moreover, multi-homing can weaken the strategic dependency between platforms and users, thereby reducing the profitability of platforms. Finally, it is observed that blockchain investment in shipping platforms brings more consumer surplus and social welfare, with moderate network effects and multi-homing behaviour.

Synergizing shared micromobility and public transit towards an equitable multimodal transportation network

This paper assesses the equity impacts of shared micromobility and investigates regulatory policies that improve transport equity and promote synergy between public transit and shared micromobility. We consider a multimodal transportation network, where a micromobility platform deploys docking stations and operates a fleet of micromobility vehicles to provide shared micromobility services and a public transit agency offers transit services over a transportation network. A market equilibrium model is developed to capture the intimate interactions among access and egress times of shared micromobility services, waiting times of transit services, the spatial distribution of docking stations, passenger demand, platform pricing and fleet sizing, vehicle repositioning and the micromobility platform profit. The platform decision problem is cast as a high-dimensional non-convex program. A solution method is proposed to efficiently compute the solution through problem reformulation and dimensionality reduction. Based on the proposed framework, we evaluate spatial equity in transport accessibility using the Gini index, and find that although shared micromobility improves overall transport accessibility, the benefits are not fairly distributed across different geographic zones, which leads to enlarged spatial inequity gaps after introducing shared micromobility. To promote transport equity, we investigate three policy directions: (a) to impose a vehicle density floor on shared micromobility; (b) to offer a subsidy on shared micromobility rides for first/last-mile connections; and (c) to promote collaboration between public transit and shared micromobility. We show that different regulatory policies have advantages and limitations. The minimum vehicle density requirement can simultaneously improve spatial equity and passengers’ surplus, but has limited equity improvements. In contrast, the subsidy on bundled services could significantly mitigate spatial inequity, but it hurts passengers and the platform profit. Compared to the other two policies, the transit-micromobility collaboration can lead to higher equity improvement, higher passenger surplus, while offering a guarantee on the platform profit, which turns out to be the most cost-effective approach. These insights are validated through realistic numerical studies for San Francisco.

Platform integration in ride-sourcing markets with heterogeneous passengers

This paper explores the impacts of a novel business model termed platform integration, which enables passengers to simultaneously request on-demand rides from multiple ride-sourcing platforms via a third-party integrator. In particular, we employ an equilibrium model where passenger demand and driver supply are endogenously dependent on the prices and wages that emerge from the competitive interaction between two platforms, with and without an integrator. A Hotelling model is adopted to characterize passengers’ heterogeneity in service preference for different platforms. We employ the concepts of a Nash equilibrium and a shared monopoly to analyze equilibrium outcomes that can arise in various settings of demand and supply characteristics with and without platform integration. We find that how the platform should adjust its price and wage at Nash equilibrium as potential demand increases is affected by the nature of supply. We also find that the profit at Nash equilibrium can increase or decrease in supply capacity depending on the competitive situation of the platforms. We build on these equilibrium results to analyze how platform integration affects the platform’s decision-making of price and wage, and market performance. We find that platform integration can increase platform profit but reduce driver income, and may hurt passengers who have a strong preference for one certain platform, especially in the case of a less heterogeneous supply.

Customer and provider bounded rationality in on-demand service platforms

The growing literature on operations management in the context of the sharing economy typically assumes that both customers and providers are fully rational. In contrast, we consider an on-demand service platform (e.g., Didi and Uber) with boundedly rational customers and providers that sets a price charged to customers and a wage paid to providers. Both customers and providers are sensitive to the payment terms set by the platform and also to congestion in the system (given by the relative numbers of available customers and providers in the market). We capture bounded rationality using a model in which customers and providers are incapable of accurately estimating the congestion level. We examine the impact of bounded rationality on the platform profit, consumer surplus, and labor welfare. We find that both customers’ and providers’ bounded rationalities may benefit the platform. Specifically, when customers’ or providers’ bounded rationality level and service valuation are relatively large or the valuation is relatively small, more irrational customers or providers increases the platform’s profit. Moreover, we find that the platform can exploit the bounded rationality differences between customers and providers to gain profit. Counterintuitively, we also demonstrate that the high bounded rationality of customers or providers may increase consumer surplus and/or labor welfare. Finally, bounded rationality on one side (e.g., customer side) can make bounded rationality on the other side (e.g., provider side) more likely to increase the platform’s profit, consumer surplus, or labor welfare under certain conditions.

Filling the theoretical gap: Analyzing the nature of platform profits by integrating the rent perspective and Marxian circulation theory

The significant increase in profits generated by platforms has sparked considerable debate regarding the nature of this income. These discussions center on two perspectives: the value-creating perspective suggests that their profits result from new forms of exploitation in the platform economy, while the rent perspective argues that the platforms extract and transfer value from other value-creating activities. While the latter perspective acknowledges that platforms do not directly engage in value creation, it fails to analyze their functions from the standpoint of Marxian circulation theory. Viewed from this standpoint, platforms represent a new form of commercial capital that propels circulation. Through their capacity to collect and process user behavior data, they attain a higher level of centralization than traditional commercial organizations, and extract value created by productive activities.

Control of dynamic ride-hailing networks with a mixed fleet of autonomous vehicles and for-hire human drivers

This study examines a ride-hailing platform that employs a mixed fleet of autonomous vehicles (AVs) and human drivers to offer on-demand ride-hailing services across a network over a finite planning horizon. In the mixed fleet, AVs have a fixed fleet size and are centrally managed by the platform, while human drivers can dynamically enter and exit from the ride-hailing market based on the prospective earning opportunities at various time periods. We introduce a game-theoretic model that encapsulates the strategic behavior of human drivers, passengers, and the ride-hailing platform. In this model, passengers choose between ride-hailing and alternative transportation options by considering fares and waiting times. Human drivers schedule their working hours in response to potential earnings and strategically relocate to maximize passenger pickups, while the platform adjusts pricing and AV repositioning strategies to maximize its profit. The solution is characterized as a Nash equilibrium within a two-player game, with one player representing the ride-hailing platform, and the other as a virtual representative of the collective human drivers. To address the non-convex nature of the game, we employ a convex relaxation technique to ascertain a near-optimal solution framed as an ϵ-Nash equilibrium, with ϵ accurately characterized. The proposed model and solution algorithm are validated in a case study for Manhattan, New York City. The simulation results underscore that the distribution of human drivers is closely aligned with the immediate, minute-to-minute fluctuations in passenger demand, enabling them to effectively meet the surge during peak demand windows. Conversely, the deployment patterns of AVs are more attuned to the extended, daily cycles of passenger movements throughout different city zones, from daytime to nighttime. We further compare the mixed fleet model with scenarios where the platform exclusively utilizes human drivers or AVs, respectively. The comparison show that the mixed fleet model excels in balancing the supply and demand over space and time, thereby leading to shorter wait times and reduced travel cost for ride-hailing passengers, as well as improved profit for the ride-hailing platform.

Asymmetric Impact of Matching Technology on Influencer Marketing: Implications for Platform Revenue

This paper examines how AI-driven matching technology impacts influencer competition and platform profitability, revealing nuanced effects on campaign pricing and platform revenue.

An incentive strategy for the retention of impatient passengers in ride-sourcing markets

A prominent problem plagued the ride-sourcing service providers is the instantaneous real-time demand–supply imbalance. This entails a terrible user experience and causes adverse sustainable results such as customer churn for the platform, which will damage both drivers’ income and the platform revenue. To reduce passenger cancellation caused by extended wait times, we propose an incentive-based queue management strategy —a discount method— to enhance the waiting experience and encourage passengers to stay in a post-peak period . We formulate the waiting process for a ride-sourcing service as an M/M/c+M queue system with impatient passengers, and model passengers’ reneging behaviors by characterizing the impact of discounted trip fare and updated queue information on their travel utilities. Based on such a behavior model, we can analyze the effect of the discount strategy on the queueing process, which allows us to maximize the platform’s profit growth by tailoring the discount strategy. Our result shows that the discount strategy can effectively increase the platform’s profit. Under the specific condition of the discount strategy, the earlier the strategy is implemented in the post-peak period, the higher platform profit can be achieved. Moreover, we explore the impact of queueing system parameters on the performance of the discount strategy and gain some interesting insights.

Asset-light or asset-heavy? Implications for ride-hailing platforms’ profits, consumer surplus, and driver surplus

Recently, ride-hailing platforms have started buying vehicles and leasing them to drivers with and without cars to expand supplies and serve more customers, switching from asset-light to asset-heavy companies. Unlike the asset-light model, where the platforms can only induce with-car drivers to use their vehicles to provide services, if the platforms switch to the asset-heavy model, they have an extra supply source that allows both with-car and without-car drivers to rent vehicles.This paper studies the impact of this switch on a ride-hailing platform’s profit, consumer surplus, and driver surplus. We consider a ride-hailing platform that decides whether to buy cars and sets a price charged to customers and a wage paid to drivers. Both customers and drivers are sensitive to the payment terms set by the platform and also to congestion in the system (given by the relative numbers of available customers and drivers in the market).Driven by these features, we find that under the asset-heavy model, as the car purchase cost increases, the platform should shift from inducing both with-car and without-car drivers to rent vehicles to inducing only without-car drivers. However, in the case where the car purchase cost is not too large, the platform should adopt the asset-heavy model, which allows both classes of drivers to rent vehicles, and choose the asset-light model otherwise. We further show that customers do not always benefit from the asset-heavy model because the corresponding total supply may be less than that of the asset-light model, leading to increased congestion. Additionally, we find that the with-car driver surplus of the asset-heavy model may be higher or lower than that of the asset-light model. Finally, if the cost is sufficiently small, when the number of potential without-car drivers is large or customers’ sensitivity to congestion is large, we show that the asset-heavy model can increase platform profit, consumer surplus, and total driver surplus (i.e., the sum of with-car and without-car drivers’ surpluses), resulting in a “win-win-win” outcome.

Navigating Platform-Led Affiliate Marketing: Implications for Content Creation and Platform Profitability

Practice- and Policy-Oriented Abstract Recently, some user-generated content (UGC) platforms have introduced shopping features to generate additional commission-based revenue from in-app transactions. These platforms allow creators to produce shoppable content, where promoted products are tagged with affiliate links, and in return, they earn a percentage of the commission the creators receive from merchants This paper aims to investigate the impact of this platform-led affiliate marketing business model on the key stakeholders. Employing a game-theoretic model, our study demonstrates that platform-led affiliate marketing can create a win-win situation for UGC platforms, creators participating in affiliate marketing, and consumers. Furthermore, even creators not participating in affiliate marketing can sometimes benefit indirectly from other creators’ participation. However, we find that platforms may not necessarily benefit from adopting this emerging business model because of potential losses in traffic revenue. These results carry significant managerial implications for platforms regarding whether and how to leverage affiliate marketing. Moreover, our findings suggest that creators may benefit from reducing their production efforts in the face of intensified competition. More importantly, optimal production decisions for creators can vary significantly, depending on specific factors contributing to heightened competition. These findings offer valuable insights that can guide creators in refining their production strategies.

Increasing driver flexibility through personalized menus and incentives in ridesharing and crowdsourced delivery platforms

AbstractThis article formulates and solves a stochastic optimization model to investigate the impact of crowdsourced platforms (e.g., ridesharing, on‐demand delivery, volunteer food rescue, and carpooling) offering small, personalized menus of requests and incentive offers for drivers to choose from. To circumvent nonlinear variable relationships, we exploit model structure to formulate the program as a stochastic linear integer program. The proposed solution approach models stochastic responses as a sample of variable and fixed scenarios, and to counterbalance solution overfitting, uses a participation ratio parameter. The problem is also decomposed and iterated among two separate subproblems, one which optimizes menus, and another, which optimizes incentives. Computational experiments, based on a ride sharing application using occasional drivers demonstrate the importance of using multiple scenarios to capture stochastic driver behavior. Our method provides robust performance even when discrepancies between predicted and observed driver behaviors exist. Computational results show that offering menus and personalized incentives can significantly increase match rates and platform profit compared to recommending a single request to each driver. Further, compared to the menu‐only model, the average driver income is increased, and more customer requests are matched. By strategically using personalized incentives to prioritize promising matches and to increase drivers' willingness to accept requests, our approach benefits both drivers and customers. Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver‐request pairs less likely to be accepted.

Modeling an on-demand meal delivery system with human couriers and autonomous vehicles in a spatial market

This paper investigates the impacts of introducing autonomous vehicles (AVs) into an on-demand meal delivery system at a strategic level. The proposed model consists of (i) a microscopic physical model describing the delivery process for bundled orders and (ii) a macroscopic network equilibrium model characterizing the interactions among customers, human couriers (HCs), and AVs, as well as couriers’ repositioning behaviors in the market. A tailored algorithm based on the Alternating Direction Method of Multiplier (ADMM) is developed to solve the platform’s optimal pricing and maximize its profit. To investigate the impact of AV operations, we test three AV distribution rules, i.e., distributing AVs evenly in the space (Rule 0), proportional to demand (Rule 1), and inversely proportional to demand (Rule 2). The numerical experiments show that Rule 2 archives the maximum platform profit, along with the highest service throughput and the hourly earning rate of HCs. Nevertheless, the numerical experiments adopting the parameters calibrated by current market conditions show that the employment of AVs does not show significant benefits to the platform or other stakeholders. It can only generate a higher platform profit when the AV operation cost is lower than HCs’ hourly earnings in a purely labor-based MDS system. As the AV fleet size expands, the improvement of service quality is rather minor meanwhile the hourly earning of HCs drops substantially.

Business expansion strategy of two-sided platform considering technology R & D effect and congestion effect

PurposeA new model for two-sided platforms is presented, incorporating both the original business and an expanded (new) business. Previous studies have neglected the impact of technology R & D on the new business aspect of two-sided platforms. This study addresses this gap by examining the technology R & D effect on the new business while also considering the congestion effect on the original business. It investigates the optimal pricing and user scales for both the original and new businesses. Additionally, the profits of the original business, the new business, and the overall two-sided platform are analyzed.Design/methodology/approachPrevious studies have overlooked the technology R & D effect on the new business aspect of two-sided platforms. Therefore, this study focuses on examining the technology R & D effect on the new business. Additionally, the congestion effect on the original business is considered. To determine the optimal prices for both sides of the original and new businesses, a game sequence model is introduced.FindingsThe optimal price on the service buyers (defined b side) of new business increases only with regard to technology R & D effect on b side of new business increasing. The optimal price on b side of new business is equal to half of the b-side R & D effect. The optimal profit of original business decreases with regard to the technology R & D effect of new business and cross-market network effect on the services provider (defined s side) between original business and new business increasing, respectively. To gain optimal profit of two-sided platform, the two-sided platform adopts some strategies to improve the congestion.Originality/valueA new two-sided platform model is stated, in which the technology R & D effect is embraced. The expanded business scenario of two-sided platform is considered, that is, the two-sided platform has one business firstly and a new business is developed or improved of two-sided platform. To solve the prices strategy of original business and new business, a game sequence of the original business and new business is presented.

Agency model versus wholesale model

Agency and wholesale models are widely adopted vertical contractual agreements. This paper compares the private incentives and social welfare of these two business models by highlighting the differences in move order and price structure. With a monopoly platform, the agency model dominates the wholesale model with respect to social welfare and the platform's profit if and only if demand is subconvex. With duopoly platforms, having both platforms adopt the agency model is socially desirable, and it is a dominant-strategy Nash equilibrium if demand is weakly convex. Our findings have novel theoretical contributions and offer insights into some influential antitrust cases.

Manufacturer encroachment in a platform supply chain: An analysis of synergy effect, dysergy effect, and mutual exclusivity effect

AbstractThe prosperity of internet sales is stimulating a phenomenal growth of retail platforms and manufacturer encroachment. Encroachment spawns channel competition, potentially threatening the platform's profit, which in turn induces a series of countermeasures by the platform. Motivated by this trend, we develop a game‐theoretical model to explore the strategic and tactical interactions between a manufacturer and a platform, in which the manufacturer determines encroachment strategy and quality differentiation tactic, and the platform determines sales mode strategy and information sharing tactic. We find that the manufacturer is profitable and the platform is damaged from encroachment. Furthermore, the manufacturer's quality differentiation tactic can propel the platform to select reselling that indirectly enhances post‐encroachment profitability. In contrast, sales mode and information sharing can be mono or jointly utilized by the platform to counter encroachment. Significantly, we provide a novel inspection method to systematically analyze the cumulative performance of the platform's two countermeasures by introducing the synergy effect, dysergy effect, and mutual exclusivity effect. Interestingly, we highlight that the performance of mono‐countermeasures may be even more effective than that of combined countermeasures to mitigate the platform's loss caused by encroachment. This finding enriches not only the analytic understanding of received wisdom about whether more countermeasures can invariably hold competition edges but also provides useful managerial guidelines for strategic interaction research. Moreover, the manufacturer's increased encroachment capabilities may promote the platform to shift from reselling to agency selling, which reveals that manufacturer encroachment may be a critical factor in the popularity of agency selling.

Pricing strategy for cross-market two-sided platforms based on joint membership system

Taking the joint membership system carried out by two cross-market bilateral platforms as the background, this paper studies how to achieve the optimal cooperation between two cross-market platforms, and analyzes the various influences brought by platform differences. Considering two cross-market two-sided platforms with differences basic services, we establish a horizontal cooperation model for cross-market bilateral platforms based on the joint membership system. Taking cross-network externalities as the starting point of the research, we compare and analyze the platform strategies under decentralized decision making and joint centralized decision making to obtain the benchmark for joint membership cooperation. Finally, we propose a two-part pricing mechanism that can make joint membership cooperation achieve the best, and analyze the impact of basic service differences on platform pricing, user participation, platform profits and coordination mechanism.

When ownership and copyright are separated: Economics of non-fungible token marketplaces with secondary markets

Creators have long strived to secure royalties for their works but with little success. In the digital realm, monetization presents an even greater challenge, as traditional digital assets frequently suffer from piracy issues, primarily due to the lack of verifiable ownership. Recently, non-fungible token (NFT), a blockchain-enabled tradable digital asset, has aroused great public attention for its potential to address this long-standing issue. Specifically, NFTs empower creators by enabling them to earn resale royalties from post-primary-sale transactions and by providing verifiable ownership that facilitates consumer trading in a secondary market. In this paper, we employ a two-stage game-theoretic model to examine this innovative business model. Here, an NFT creator makes optimal pricing and royalty rate decisions, while consumers make their purchase and resale decisions accordingly. Our study reveals that the creator inevitably reduces the selling price when a secondary market is introduced, even without imposing royalties. Moreover, contrary to conventional wisdom that NFT-enabled royalties always benefit creators, our research uncovers that the introduction of a secondary market leads to unintended revenue loss for the creator, particularly when most consumers only engage in secondary transactions. Furthermore, we find that the platform's profitability diminishes with the introduction of a secondary market, especially when most consumers are uninformed and the platform relies primarily on the primary market for commission collection. Finally, we find that the introduction of a secondary market may leave consumers worse off, despite the resale opportunities it offers. Our findings carry crucial managerial implications for platforms, creators, consumers, and policymakers.

Sales model and logistics selections for a manufacturer considering carbon emission and logistics levels

Improving logistics levels requires mobilizing more resources, resulting in more carbon emissions. We consider a manufacturer selling its products through an e-commerce platform that sells them to customers (reselling) or directly accessing customers via the platform by paying a proportional fee (agency selling). Under reselling, the manufacturer first sets a wholesale price and then the platform sets a retail price and a logistics level. Under agency selling, if using third-party logistics, the manufacturer sets the retail price and logistics level. When using the platform logistics, the manufacturer sets the retail price and the platform sets the logistics level; moreover, the manufacturer bears part of the logistics cost. This study examines the manufacturer’s sales model and logistics selection and its impact on the platform’s profit and consumer surplus. We find that if the logistics-improving efficiency is sufficiently high (low), the manufacturer should employ reselling (agency selling and third-party logistics) when the manufacturer bears a sufficient small or large part of the logistics cost. However, regardless of the size of the logistics-improving efficiency, the manufacturer should adopt agency selling and platform logistics when the logistics cost borne by the manufacturer is intermediate. If consumers become more sensitive to carbon emission levels, the manufacturer is less (more) likely to adopt the reselling model and platform logistics (agency selling model and platform logistics). Choosing reselling or agency selling and platform logistics, or agency selling and third-party logistics could benefit both the platform and consumers, generating a “win-win-win” outcome.