Profit Of Platform Research Articles

Getting more third-party participants on board: Optimal pricing and investment decisions in competitive platform ecosystems

In two-sided markets, hardware/software platforms meet the unified and personalized demands of consumers through hardware and software products respectively, forming their own ecosystems. For platform enterprises, how to drive third-party value creation through interface extensibility investment (typically on application programming interfaces, or APIs) under different pricing schemes, has become a critical issue under intensified competition. In this paper, we build game theoretic models in four platform competition scenarios (NR, NRT, IR, IRT) considering both investment decisions—no investment (N), investment (I), and pricing decisions—registration fee only (R), registration fee and transaction fee (RT), and analyze the competitive equilibrium in each scenario. Our results show that: (1) Although “reducing price” and “enhancing investment” are partial substitutes, platforms are still incentivized to implement both strategies. Contrary to the traditional wisdom, when platforms with investment charge both registration and transaction fees, the strengthening of cross-side network effect may lead to an increase in registration fee on both sides. (2) The improvement of interface extension effectiveness (cost reduction for developers per unit of investment from platforms) shifts more developers from single-homing to multi-homing, leading to fiercer market competition and thus lower platform profit in equilibrium. (3) Although implementing investment may not be optimal from the profit perspective, it does help platforms in securing a larger market share on the developer side. (4) The existence of same-side network effect enhances both side users’ incentive to enter the market; however, for the platforms, they should carefully mind the irrational price competition to avoid falling into profit-hurting traps.

Manufacturer's Encroachment and Carbon Emission Reduction Decisions Considering Cap-and-Trade Regulation and Consumers' Low-Carbon Preference.

Carbon emission reduction and achieving carbon neutrality has become an inevitable trend in the sustainable development era. We investigate the manufacturer’s encroachment and carbon emission reduction decisions considering government cap-and-trade regulations and consumers’ low-carbon preference. The equilibrium decisions for the four scenarios are analytically obtained and compared based using the Stackelberg game. A comparison with and without cap-and-trade regulation under two encroachment decisions regarding member’s profits and carbon emission reduction levels are conducted. It is shown that the encroachment decision is always advantageous for the manufacturer if the government decides not to implement cap-and-trade regulation, and the retailer always loses profit. Moreover, if the carbon quota is sufficient, cap-and-trade regulation benefits the manufacturer. Otherwise, the manufacturer’s encroachment decision depends on the appropriate initial unit amount of carbon emission and unit carbon price. The retailer’s profit may not always be hurt by the manufacturer’s encroachment with cap-and-trade regulation; unless the unit carbon price exceeds a certain threshold, a higher consumer’s low-carbon preference in the encroachment scenario reduces more carbon emissions than in the no-encroachment scenario for the manufacturer. Further, the rising platform commission rate causes the platform profit to increase first and then decrease; the platform profit will slightly decrease if both products become more substitutes.

Video‐sharing platform's optimal monetary incentive decisions considering motivation crowding‐out effect

Considering the motivation crowding‐out effect of monetary incentives, a game‐theoretic model is built to examine a video‐sharing platform's optimal incentive decisions. Results show that offering monetary incentives to contributors does not ensure an improvement in video quality nor an increase in the platform's profit. We identify four ideal market conditions for the platform to offer monetary incentives. We find that though the motivation crowding‐out effect seems to undermine the effectiveness of monetary incentives, the ideal market conditions for offering monetary incentives do not always require this effect to be very weak; sometimes it even has to be medium.

Two-sided pricing strategies for a parking sharing platform: Reselling or commissioning?

A parking sharing platform plays the role of a matchmaker between parking ‘slot owners/sharers’ and ‘slot renters’ who demand parking. The platform can act as either a reseller or a commissioner, and can have different economic objectives when setting prices for owners and renters. This paper is the first to address a platform operator’s pricing strategy in a two-sided parking sharing market under different business formats (reselling and commissioning) and different economic objectives (profit-maximization and social welfare-maximization). The number of parking slots rented out is assumed to be an increasing function of the number of suppliers (owners) who offer a slot for rent, and the number of demanders (parkers) who request a slot. We derive the supply–demand matching equilibrium and the platform’s two-sided pricing strategies for the alternative business formats and objectives. We also analytically and numerically examine how the buying and selling prices, and numbers of participating owners, renters, and transactions vary with parameter values in each business format. We find that a welfare-maximizing platform earns a positive profit if the matching function has decreasing returns to scale, and incurs a deficit if the matching function has increasing returns to scale. If the cost of inconvenience to slot owners from renting a slot equals their cost of disappointment from failing to rent it out, then the reselling and commissioning formats can replicate each other (i.e, they can yield identical platform profit and social welfare). If the cost of inconvenience is greater then the commissioning format is superior, whereas if the disappointment is greater the reselling format is preferred.

Platform subsidy policy design for green product diffusion

With the advent of information technology, online platforms have become an vital intermediary for acquiring green information and purchasing green products. This study uses two-sided market theory to estimate the size of the green market for green subsidies under various targets. First, we compare the differences in the equilibrium market size and platform profits for three scenarios (no subsidy, maximum platform profits, and maximum social welfare). In addition, we measure the basic conditions for start-up subsidy schemes. Finally, we employ numerical analysis to explore how other critical factors, including cross-network externalities, product pricing, and research and development (R&D) costs, impact the market size in three scenarios. The result demonstrates how online platforms use cross-side network externalities to design asymmetric pricing strategies or subsidy policies to develop the market for green products. It has managerial implications for online platform owners or politicians in subsidizing the expansion of green products.

The entry quality threshold setting and commission rate contract selection of a peer-to-peer service sharing platform

PurposeIn recent years, some peer-to-peer (P2P) service sharing platforms have improved their service quality by setting an entry quality threshold for service providers. Considering consumers' heterogeneous preferences for service quality and commission rate, it is worth studying how to select the commission rate contract for a P2P platform under a predetermined entry quality threshold for service providers.Design/methodology/approachIn this study, the platform's profit-maximizing model is constructed under two proposed contracts: unilateral commission rate contract and bilateral commission rate contract. The optimal entry quality threshold and the optimal commission rate are obtained. This study also explores the impacts of cross-side network externality and service price on a platform's optimal decisions and social welfare.FindingsResults show that it is always advantageous for the platform to adopt the bilateral commission rate contract, which is closely related to the strength of cross-side network externality, service price and quality sensitivity coefficient. Under certain conditions, adopting the unilateral commission rate contract can reduce platform profit and service provider surplus, and improve consumer surplus and social welfare.Originality/valueThis study analyzes the unilateral commission rate contract and the bilateral commission rate contract of the platform, and discusses which contract is beneficial to the platform, consumers and service providers. In addition, this study provides a basis for the operation decision of a P2P service sharing platform and the pricing decision of service providers.

Review on shoe identification and profit model

With the rise of sneaker culture, the shoe resale market is huge, which has led to a large number of resale trading platforms. This article mainly analyzes the market development status of shoes and the importance of shoe appraisal service in the resale platform, then summarizes the shoe appraisal method, analyzes the core resale platform profit model, and take the platform, for example, analyzes the transaction commission, community traffic cash, technical service fee and other main profit business, again on the basis of the future development direction.

Customer Satisfaction, Advertising Competition, and Platform Performance

We study how customer satisfaction affects platforms’ advertising competition and their equilibrium market shares and profits. Consider a market with multiple competing (online) platforms, all of which decide advertising outlay to maximize their own average profit. A customer chooses which platform to visit depending on her experience of the last purchase and on the platforms’ advertising outlays. After purchasing from a platform, with certain probability (satisfaction probability), the customer would be satisfied. A satisfied customer is more likely to repurchase from the platform than if she is dissatisfied. There are two types of customers in the market: loyal and switcher. Conditional on either satisfaction outcome, the loyal is more likely to repurchase from the last‐visited platform than the switcher. In the duopoly, we show that lower a platform's customer satisfaction or a higher proportion of switcher may alleviate the advertising competition. Consequently, a platform does not necessarily get a higher profit when its customer satisfaction is higher; and the platform with higher (lower) customer satisfaction benefits from a larger proportion of loyal (switcher). We then study the case with more than two competing platforms. Under the symmetric market, the equilibrium advertising outlay first increases and then decreases as the number of platforms increases. Every platform spends more on advertising and gets a lower profit when customer satisfaction improves. The equilibrium profit of platform is always increasing in the proportion of switcher. Under the fragmented market, we find that similar to the duopoly, the platforms with higher (lower) customer satisfaction benefit more when facing the loyal (switcher) than when facing the switcher (loyal). Several extensions are considered: two‐period impact of purchasing experience, an outside option for customers, endogenous satisfaction probabilities, and asymmetric information between platforms.

Dynamic pricing and penalty strategies in a coupled market with ridesourcing service and taxi considering time-dependent order cancellation behaviour

Time-dependent order cancellation behaviour affects ridesourcing platform operation and overall system performance. This paper models impacts of time-dependent order cancellation behaviour on the driver–rider matching efficiency and examines ridesourcing platform operation strategies, where taxi is the alternative to the ridesourcing mode. A time-dependent matching model is developed to characterize the complex interaction among participants (drivers and riders) considering ridesourcing order cancellation behaviour, where the impact of the cancelled orders on the numbers of drivers and riders is explicitly modelled. In particular, we formulate the time-dependent order cancellation rates before and after the matching stage (i.e., cancellation of unconfirmed and confirmed orders). Under the proposed model, a platform profit maximization problem is formulated and three pricing strategies are examined. Our numerical studies demonstrate that the dynamic pricing (i.e., customer/rider fare and driver wage) can well accommodate time-dependent system inputs (e.g., demand rates) and thus enable the platform to increase profit via better market segmentation. We also investigate the objective of maximizing the number of completed trips and examine the trade-off between the platform profit and the number of completed trips. In addition, we show that the relaxation of upper bounds of the ridesourcing fare and order cancellation penalty can increase the ridesourcing platform’s profit and indirectly improve the utilization rate of taxis as well as the taxi company’s profit.

Pricing Analysis of Online Shopping Platforms Considering Consumer Information Levels.

To address the problem of frequent dishonest transactions by online shopping platform merchants, we developed monopoly and competitive platform pricing models based on two-sided market theory, which introduce consumer information levels. This article analyzes the incentives of the platforms to improve consumer information levels in platform pricing strategies. Monopoly online shopping platforms aim to maximize profits. The higher the consumer information level is, the lower the fees charged to merchants; this can lead to increased platform profits. The charging of consumers depends on cross-network externalities. Competitive online shopping platforms also aim at maximizing profits. Under the circumstance that the number of consumers remains the same, the higher the consumer information level is, the more merchants the platforms will attract. This reduces bilateral user fees, and platform profits will be lower. From the perspective of consumer information level, the article analyzes the impact of monopoly and competitive platforms adopting return measures to improve the level of consumer information on platform pricing, number of bilateral users, and profits.

Platform Tokenization: Financing, Governance, and Moral Hazard

This paper highlights two channels through which blockchain-enabled tokenization can alleviate moral hazard frictions between founders, investors, and users of a platform: token financing and decentralized governance. We consider an entrepreneur who uses outside financing and exerts private effort to build a platform and users who decide whether to join in response to the platform’s dynamic transaction fee policy. We first show that raising capital by issuing tokens rather than equity mitigates effort under-provision because the payoff to equity investors depends on profit, whereas the payoff to token investors depends on transaction volume, which is less sensitive to effort. Second, we show that decentralized governance associated with tokenization eliminates a potential holdup of platform users, which in turn alleviates the need to provide users with incentives to join, reducing the entrepreneur’s financing burden. The downside of tokenization is that it puts a cap on how much capital the entrepreneur can raise. Namely, if tokens are highly liquid, that is, they change hands many times per unit of time, their market capitalization is small relative to the net present value (NPV) of the platform profits, limiting how much money one can raise by issuing tokens rather than equity. If building the platform is expensive, this can distort the capacity investment. The resulting tradeoff between the benefits and costs of tokenization leads to several predictions regarding adoption. This paper was accepted by Vishal Gaur, operations management.

Designing van-based mobile battery swapping and rebalancing services for dockless ebike-sharing systems based on the dueling double deep Q-network

Ebike-sharing (electric bicycle-sharing) systems are gaining popularity as ebikes provide riders with transportation convenience when people have limited accessibility to other travel modes. Compared to traditional bike-sharing systems, ebike-sharing systems are more complicated as the platform needs to handle battery recharging issues as well as the imbalance between supply and demand. However, previous studies have not discussed how to address the two issues effectively. In this paper, we consider a dockless ebike-sharing system with removable ebike batteries and introduce vans to such a system to solve recharging and rebalancing problems simultaneously. In other words, during the operational horizon, van drivers can choose among rebalancing tasks, battery swapping tasks, and half-rebalancing-half-swapping tasks. This paper utilizes the Markov decision process to depict the highly dynamic ebike-sharing system with a platform player (agent) and a representative van driver player (agent). In such a system, van drivers choose their tasks to maximize their income, and the platform allocates spatiotemporal subsidies with predefined subsidy amounts to incentivize van drivers and optimize the profit. To efficiently solve the dynamic optimization problem with mixed agents, we apply the dueling double deep Q-network method which is an advanced reinforcement learning approach. We conduct numerical studies based on a real-world dataset in New York City, and evaluate the performance of the proposed operational services under different schemes. Our results show that the proposed van-based services for battery swapping and rebalancing could help increase the platform's profit and van drivers' earnings, and improve system performance. Additionally, it is also proved that the platform, van drivers, and the overall ebike-sharing system all benefit from spatiotemporal subsidies.

Research on Platform Operation Strategy Considering Consumers’ Hassle Costs

Based on consumers’ different preferences for hassle costs, we study two platform operation strategies: selected platforms and diversified platforms. Considering diverse charging systems of merchants on the platform, a two-sided user utility function and profit function are established to examine the influence of hassle costs, platform services and the strength of two-sided network effects on the scale of platform users, and platforms’ profits and price. The results show that: (1) The selected platform strategy adopting the transaction fee system is better than other strategies. (2) Under the selected platform strategy, the scale of bilateral users and platform profits will decrease with the increase in hassle costs, and increase with the strengthening of the bilateral network effects. However, the proportion of equilibrium pricing for merchants will increase with the increase in consumer hassle costs, and will decrease with the increase in the network effect on the consumer side. (3) The less value-added services that selected platforms provide to consumers, the more value-added services exist to merchants and the higher the equilibrium pricing is for merchants. However, as the network effect on the side of merchants is increasing, the equilibrium pricing ratio of the platform to merchants shows three trends However, the general trend is that the greater the network effect of the business side, the lower the fee ratio and the higher the platform profit.

Shared parking for ride-sourcing platforms to reduce cruising traffic

Ride-sourcing platforms create cruising traffic that exacerbates road congestion in large cities. This paper considers shared parking services for ride-sourcing vehicles to reduce the cruising traffic. We propose a novel business model that integrates shared parking services into the ride-sourcing platform. In the proposed business model, the platform (a) provides app-based interfaces to connect passengers, drivers and garage operators (private or commercial garages); (b) matches passengers to ride-sourcing vehicles for ride services; (c) matches idle vehicles to vacant parking spaces to reduce cruising traffic; and (d) determines the ride fare, driver payment, and parking rates to maximize its profit. We model the integrated platform as a three-sided market (i.e., passenger, driver and garage) that involves two matching processes: passenger-driver matching and driver-garage matching. An economic equilibrium model is proposed to characterize the incentives of passengers, drivers, garage operators, and the ride-sourcing platform. Optimal pricing strategies and the corresponding market outcomes are derived by solving the platform’s profit maximization problem. Based on the model, we show that integrated parking and ride-sourcing service can offer a Pareto improvement: in certain regime, it leads to higher passenger surplus, higher driver surplus, higher garage surplus, higher platform profit, and reduced cruising traffic. Further, we investigate the impacts of matching efficiency between drivers and garages on the proposed business model. We show that a higher matching efficiency always leads to increased platform profit and driver surplus, but it may not necessarily benefit passengers or garage operators in all cases. The above insights are validated through realistic numerical studies for San Francisco.

Ride-Hailing Platforms: Competition and Autonomous Vehicles

Problem definition: Ride-hailing platforms, which are currently struggling with profitability, view autonomous vehicles (AVs) as important to their long-term profitability and prospects. Are competing platforms helped or harmed by platforms’ obtaining access to AVs? Are the humans who participate on the platforms—driver-workers and rider-consumers (hereafter, agents)—collectively helped or harmed by the platforms’ access to AVs? How do the conditions under which access to AVs reduces platform profits, agent welfare, and social welfare depend on the AV ownership structure (i.e., whether platforms or individuals own AVs)? Academic/practical relevance: AVs have the potential to transform the economics of ride-hailing, with welfare consequences for platforms, agents, and society. Methodology: We employ a game-theoretic model that captures platforms’ price, wage, and AV fleet size decisions. Results: We characterize necessary and sufficient conditions under which platforms’ access to AVs reduces platform profit, agent welfare, and social welfare. The structural effect of access to AVs on agent welfare is robust regardless of AV ownership; agent welfare decreases if and only if the AV cost is high. In contrast, the structural effect of access to AVs on platform profit depends on who owns AVs. The necessary and sufficient condition under which access to AVs decreases platform profit is high AV cost under platform-owned AVs and low AV cost under individually owned AVs. Similarly, the structural effect of access to AVs on social welfare depends on who owns AVs. Access to individually owned AVs increases social welfare; in contrast, access to platform-owned AVs decreases social welfare—if and only if the AV cost is high. Managerial implications: Our results provide guidance to platforms, labor and consumer advocates, and governmental entities regarding regulatory and public policy decisions affecting the ease with which platforms obtain access to AVs.

Platform competition and consumer foresight: The case of airports

This paper studies the effect of competition and consumer foresight on platform profits. The focus is on airports, which provide passengers with aeronautical and commercial services through airlines and retailers. Our results can be summarized as follows. First, we unravel the relationship between consumer foresight and the optimal pricing of the two services. When passengers are myopic, they undervalue the surplus they derive from the retail services, so that the airport charges low landing fees and makes profits from the retail business. When passengers are foresighted, they better anticipate the surplus from the retail services, so that the airport changes its strategy by charging higher landing fees and boosting competition in the retail sector. Second, we find that the relationship between profits and consumer foresight strictly depends on the considered market structure. When the airport has no competitors, airport profits are non-decreasing in the degree of consumer foresight. By contrast, under duopoly competition, a weakly-negative correlation between airport profits and consumer foresight is observed. These results allow to derive two main managerial implications. First, airport competition can lead to higher landing fees. Second, under competition, an airport is not necessarily interested in informing passengers about its retail facilities. However, an extension where airports decide whether to set an advertising campaign to inform passengers about their retail facilities reveals that they end up locked in a Prisoner’s Dilemma.

B2C online ride-hailing pricing and service optimization under competitions

<p style='text-indent:20px;'>B2C online ride-hailing is to provide customers with anytime, anywhere and on-call ride services by professional vehicles and professional drivers. How to maintain good service quality and reasonable pricing under competition is of importance to a platform. In this paper, we will integrate pricing and service together to maximize the profit of a platform through Nash game theory. Specifically, we will establish the models for there scenarios: the demand market competition under decline of ride demand, the supply market competition under surge of ride demand, and the coexistence of demand and supply market competition for stable ride demand. Then, the Nash equilibriums are derived for the three models which are corresponding to minimize ride price, optimize quality of efforts and maximize profit. Our results uncover that the driver's incentive amount is conducive to the profit of both platform and the drivers for the case of demand market competition. The platforms and drivers achieve the highest profit under supply market competition, and the strategy through minimizing price and maximizing service can effectively adjust the balance between market supply and demand.</p>

A Bi-objective Model for the Cloud Manufacturing Configuration Design with Resilience and Disruption Risks

Given the fourth industrial revolution (i.e., Industry 4.0), a cloud manufacturing (CMfg) system is introduced as a modern service- and customer-centered manufacturing paradigm. This system incorporates the distributed manufacturing corporations to collaborate as an interconnected system and share their production capabilities or resources without any stoppages. In this regard, this paper develops a novel two-stage bi-objective mathematical model for designing resilient CMfg configuration by maximizing the platform's profit and maximizing the resilience level of the designed CMfg network. Three primary resilience indicators, including design quality, proactive capability, and reactive capability, are considered in the proposed model. Moreover, to evaluate the resilience level of the designed CMfg network, a new objective function is developed. An ε-constraint augmented (AUGMECON2) method is used to solve the bi-objective model. Furthermore, a simple maximum-likelihood sampling (MLS) method is used to reduce the number of possible scenarios. Finally, some in-depth analyses are carried out to illustrate and validate the performance of the proposed solution approach.

Pricing Strategies of Registration System and Transaction System in Live Broadcast Platform

This paper investigates a supply chain consisting of the monopoly live broadcast platform, content producers, and consumers. Based on the two‐sided market theory, the platform’s pricing strategies and their analysis are considered under the registration system and the transaction system. Firstly, it shows that platform users’ scale and profit are positively correlated with the intergroup network externality from both sides and the intragroup network externality inside the consumer group and negatively correlated with the intragroup network externality inside the content producer group. Secondly, the platform profit, registration fee charged to content producers, and transaction fee charged to consumers are negatively correlated to the content production cost and positively related to consumers’ content quality sensitivity coefficient. When consumers are more sensitive to content quality, they are positively correlated to content quality. Finally, the registration fee charged by the platform to consumers is positively correlated with the content production cost, negatively correlated with the content quality level, and positively associated with the consumer content quality sensitivity coefficient.

A Stackelberg Game Approach for Managing AI Sensing Tasks in Mobile Crowdsensing

Mobile Crowdsensing (MCS) is a new paradigm that leverages the collective sensing ability of a crowd so that a special task can be performed through the aggregation of information collected from personal mobile devices. While MCS brings several benefits, its application is prevented by challenges such as the efficient recruitment of users, effective mechanisms for rewarding users to encourage participation, and an effective and fast enough approach for managing the underlying resources that support large-scale MCS applications involving a large number of people in data collection. On the other hand, Artificial Intelligence (AI) applications, which are mostly based on Deep Neural Networks (DNN), are becoming pervasive today and are executed by the end users’ mobile devices, which are characterised by limited memory and computing power, and low battery level. This paper describes and evaluates an incentive mechanism for a mobile crowdsensing system with an AI sensing task based on a one-leader multi-follower Stackelberg game. The MCS platform, as a leader, provides an AI sensing task to be executed by a DNN, which can be deployed in two different ways: fully on the user device or partially on the device and partially on edge or cloud resources. The users, as followers, make their decisions regarding their participation to the MCS system and select their desired deployment given the energy and memory available on their device and the deployment reward proposed by the MCS platform. The goals of the MCS platform are: i) to motivate the users to participate in the system, ii) to maximize its profit, and iii) to identify the optimal resources supporting the sensing task that minimizes the cost and provide performance guarantees. This problem has been formulated as a mixed integer nonlinear program and propose an efficient algorithmic approach to solve it quickly. The proposed approach has been compared with some baseline methods and with BARON state-of-the-art solver. Results show that our approach converges to the optimal solution much faster than BARON (up to orders of magnitude) especially in large scale systems. Furthermore, the comparison to the baseline methods shows that our approach always beats the best baseline method under different scenarios providing up to 16% improvement for the MCS platform profit.