• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.86-96
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• 2015

In mobile communication, mobile services [MSs] (e.g., phone calls, short/multimedia messages, and Internet data) incur a cost to both mobile users (MUs) and mobile service providers (MSPs). The proposed model MobPrice consists of dynamic data pricing schemes for mobile communication in order to achieve optimal usage of MSs at minimal prices. MobPrice inspires MUs to subscribe MSs with flexibility of data sharing and intra-peer exchanges, thereby reducing overall cost. The main contributions of MobPrice are three-fold. First, it proposes a novel k-level data-pricing (kDP) scheme for MSs. Second, it extends the kDP scheme with the notion of service-sharing-based pricing schemes to a collaborative peer-to-peer data-pricing (pDP) scheme and a cluster-based data-pricing (cDP) scheme to incorporate the notion of 'cluster' (made up of two or more MUs) in mobile communication. Third, our performance study shows that the proposed schemes are indeed effective in maximizing MS subscriptions and minimizing MS's price/user.

• Architectural research
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• v.19 no.3
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• pp.71-77
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• 2017

Dynamic pricing refers to a system in which a tariff varies, according to a level of charging and applied time depending on time change. The power billing system used in the Korean Electric Power Corporation (KEPCO) is based on time of use (TOU) pricing, which is one of the dynamic pricing systems. This paper aimed to determine the operational results of a variable refrigerant flow system, to which a new control algorithm was applied, in order to respond to dynamic pricing, in summer and the utility of the new control. To do this, real measured data was acquired from a VRF system installed in a building for educational purposes, where dynamic pricing was applied for about 100 days during summer time. At the maximum load operation time period in TOU, the new control minimized operation within the indoor comfort range, an increase in refrigerant evaporation temperature in the indoor unit and the number of revolutions in a compressor in the outdoor unit was limited. As a result, power usage was decreased by 11%, and the operational cost by 14.6%. Furthermore, measurement results using the Predicted Mean Vote (PMV) model, that represented satisfaction of thermal environment, showed that 82.8% to 90.4% of the occupants of the building were satisfied during operation when the new control was applied.

• Asia Marketing Journal
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• v.16 no.1
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• pp.35-69
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• 2014

The impact of competition on pricing has been studied in the context of counterfactual merger analyses where expected optimal prices in a hypothetical monopoly are compared with observed prices in an oligopolistic market. Such analyses would typically assume static decision making by consumers and firms and thus have been applied mostly to data obtained from consumer packed goods such as cereal and soft drinks. However such static modeling approach is not suitable when decision makers are forward looking. When it comes to the markets for durable products with indirect network effects, consumer purchase decisions and firm pricing decisions are inherently dynamic as they take into account future states when making purchase and pricing decisions. Researchers need to take into account the dynamic aspects of decision making both in the consumer side and in the supplier side for such markets. Firms in a two-sided market typically subsidize one side of the market to exploit the indirect network effect. Such pricing behaviors would be more prevalent in competitive markets where firms would try to win over the battle for standard. While such qualitative expectation on the relationship between pricing behaviors and competitive structures could be easily formed, little empirical studies have measured the extent to which the distinct pricing structure in two-sided markets depends on the competitive structure of the market. This paper develops an empirical model to measure the impact of competition on optimal pricing of durable products under indirect network effects. In order to measure the impact of exogenously determined competition among firms on pricing, we compare the equilibrium prices in the observed oligopoly market to those in a hypothetical monopoly market. In computing the equilibrium prices, we account for the forward looking behaviors of consumers and supplier. We first estimate a demand function that accounts for consumers' forward-looking behaviors and indirect network effects. And then, for the supply side, the pricing equation is obtained as an outcome of the Markov Perfect Nash Equilibrium in pricing. In doing so, we utilize numerical dynamic programming techniques. We apply our model to a data set obtained from the U.S. video game console market. The video game console market is considered a prototypical case of two-sided markets in which the platform typically subsidizes one side of market to expand the installed base anticipating larger revenues in the other side of market resulting from the expanded installed base. The data consist of monthly observations of price, hardware unit sales and the number of compatible software titles for Sony PlayStation and Nintendo 64 from September 1996 to August 2002. Sony PlayStation was released to the market a year before Nintendo 64 was launched. We compute the expected equilibrium price path for Nintendo 64 and Playstation for both oligopoly and for monopoly. Our analysis reveals that the price level differs significantly between two competition structures. The merged monopoly is expected to set prices higher by 14.8% for Sony PlayStation and 21.8% for Nintendo 64 on average than the independent firms in an oligopoly would do. And such removal of competition would result in a reduction in consumer value by 43.1%. Higher prices are expected for the hypothetical monopoly because the merged firm does not need to engage in the battle for industry standard. This result is attributed to the distinct property of a two-sided market that competing firms tend to set low prices particularly at the initial period to attract consumers at the introductory stage and to reinforce their own networks and eventually finally to dominate the market.

• Journal of Distribution Science
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• v.20 no.7
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• pp.11-22
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• 2022

Purpose: Since the emergence of the Covid-19 pandemic, almost all the hospitality industry has experienced a decrease in the distribution of room occupancy. Therefore, this study aims to examine how to improve the performance of 5-star hotels in Bali by involving market orientation, revenue management orientation, competitive advantage, dynamic capability, and pricing capability. Research design, data and methodology: This study involved 127 managers in 62 five-star hotels in Bali. Analysis of this study using structural equation modelling (SEM) with SmartPLS software. Results: This study reveals that the performance of five-star hotels in Bali is influenced by factors such as market orientation, revenue management orientation, competitive advantage, dynamic capability, and pricing capability. In addition, revenue management orientation, competitive advantage, and dynamic capability have been shown to mediate the effect of market orientation on the performance of five-star hotels in Bali. Finally, pricing capability has been proven to have not been able to increase the revenue and performance of five-star hotels in Bali. Conclusions: Hotel performance is largely determined by several important factors which include market orientation, revenue management orientation, competitive advantage, dynamic capability, and pricing capability. This study provides important implications for hospitality practitioners to improve the distribution of hotel performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.627-638
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• 2014

In this paper, we study the joint price and power allocation in spectrum sharing macro-femtocell networks. The proposed game theoretic framework is based on bi-level Stackelberg game where macro base station (MBS) works as a leader and underlaid femto base stations (FBSs) work as followers. MBS has fixed data rate and imposes interference price on FBSs for maintaining its data rate and earns revenue while FBSs jointly adjust their power for maximizing their data rates and utility functions. Since the interference from FBSs to macro user equipment is kept under a given threshold and FBSs compete against each other for power allocation, there is a need to determine a power allocation strategy which converges to Stackelberg equilibrium. We consider two cases for MBS power allocation, i.e., fixed and dynamic power. MBS can adjust its power in case of dynamic power allocation according to its minimum data rate requirement and number of FBSs willing to share the spectrum. For both cases we consider uniform and non-uniform pricing where MBS charges same price to all FBSs for uniform pricing and different price to each FBS for non-uniform pricing according to its induced interference. We obtain unique closed form solution for each case if the co-interference at FBSs is assumed fixed. And an iterative algorithm which converges rapidly is also proposed to take into account the effect of co-tier interference on interference price and power allocation strategy. The results are explained with numerical simulation examples which validate the effectiveness of our proposed solutions.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.658-669
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• 2016

The tremendous increase in mobile data traffic coupled with fierce competition in wireless industry brings about spectrum scarcity and bandwidth fragmentation. This inevitably results in asymmetric-valued long term evolution (LTE) spectrum allocation that stems from different timing for twice improvement in capacity between competing operators, given spectrum allocations today. This motivates us to study the economic effects of asymmetric-valued LTE spectrum allocation. In this paper, we formulate the interactions between operators and users as a hierarchical dynamic game framework, where two spiteful operators simultaneously make spectrum acquisition decisions in the upper-level first-price sealed-bid auction game, and dynamic pricing decisions in the lower-level differential game, taking into account user subscription dynamics. Using backward induction, we derive the equilibrium of the entire game under mild conditions. Through analytical and numerical results, we verify our studies by comparing the latest result of LTE spectrum auction in South Korea, which serves as the benchmark of asymmetric-valued LTE spectrum auction designs.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.513-522
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• 2016

We propose a mechanism for monitoring load in quality of service (QoS)-enabled wireless networks and show how it can be used for network management as well as for dynamic pricing. Mobile network traffic, especially video, has grown exponentially over the last few years and it is anticipated that this trend will continue into the future. Driving factors include the availability of new affordable, smart devices, such as smart-phones and tablets, together with the expectation of high quality user experience for video as one would obtain at home. Although new technologies such as long term evolution (LTE) are expected to help satisfy this demand, the fact is that several other mechanisms will be needed to manage overload and congestion in the network. Therefore, the efficient management of the expected huge data traffic demands is critical if operators are to maintain acceptable service quality while making a profit. In the current work, we address this issue by first investigating how the network load can be accurately monitored and then we show how this load metric can then be used to provide creative pricing plans. In addition, we describe its applications to features like traffic offloading and user satisfaction tracking.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.695-708
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• 2022

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying 𝛽-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated 𝛽 time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

• Architectural research
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• v.17 no.3
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• pp.109-115
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• 2015

The dynamic pricing of electricity, where the electricity rate increases in a time zone with a high demand for electricity is typically applied to a building whose power reception capacity is greater than a certain size. This includes the time of use(TOU) electricity pricing in Korea which can induce the effect of reducing the power demand of a building. Meanwhile, a VRF (Variable Refrigerant Flow) system that uses electricity is regarded as one of the typical heating and cooling systems along with central air conditioning (central HVAC) for its easy operation and application to the building. Thus, to reduce power energy and operating costs of a building in which the TOU and VRF systems are applied simultaneously, we suggested a control for changing the indoor temperature setting within the thermal comfort range or limiting the rotational speed of an inverter compressor. In this study, to describe the features of the above-mentioned control and verify its effects, we evaluated the results obtained from the analysis of its operation data. Through the actual measurements in winter operations for 73 days since mid- December 2014, we confirmed a reduction of 10.9% in power energy consumption and 12.2% in operating costs by the new control. Also, a reduction of 13.3% in power energy consumption was identified through a regression analysis.