• Journal of Distribution Science
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• v.13 no.8
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• pp.61-71
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• 2015

Purpose - Before the year 2000, the housing prices in Korea were increasing every decade. After 2000, for the first time, Korea experienced a decrease in housing prices, and the repetitive cycle of price fluctuation started. Such a "boom and bust cycle" is a worldwide phenomenon. The current study proposes a mathematical model to explain price fluctuation cycles based on the theory of consumer psychology. Specifically, the model incorporates the effects of buyer expectations of future prices on actual price changes. Based on the model, this study investigates various independent variables affecting the amplitude of price fluctuations in housing markets. Research design, data, and methodology - The study provides theoretical analyses based on a mathematical model. The proposed model uses the following assumptions of the pricing mechanism in housing markets. First, the price of a house at a certain time is affected not only by its current price but also by its expected future price. Second, house investors or buyers cannot predict the exact future price but make a subjective prediction based on observed price changes up to the present. Third, the price is determined by demand changes made in previous time periods. The current study tries to explain the boom-bust cycle in housing markets with a mathematical model and several numerical examples. The model illustrates the effects of consumer price elasticity, consumer sensitivity to price changes, and the sensitivity of prices to demand changes on price fluctuation. Results - The analytical results imply that even without external effects, the boom-bust cycle can occur endogenously due to buyer psychological factors. The model supports the expectation of future price direction as the most important variable causing price fluctuation in housing market. Consumer tendency for making choices based on both the current and expected future price causes repetitive boom-bust cycles in housing markets. Such consumers who respond more sensitively to price changes are shown to make the market more volatile. Consumer price elasticity is shown to be irrelevant to price fluctuations. Conclusions - The mechanism of price fluctuation in the proposed model can be summarized as follows. If a certain external shock causes an initial price increase, consumers perceive it as an ongoing increasing price trend. If the demand increases due to the higher expected price, the price goes up further. However, too high a price cannot be sustained for long, thus the increasing price trend ceases at some point. Once the market loses the momentum of a price increase, the price starts to drop. A price decrease signals a further decrease in a future price, thus the demand decreases further. When the price is perceived as low enough, the direction of the price change is reversed again. Policy makers should be cognizant that the current increase in housing prices due to increased liquidity can pose a serious threat of a sudden price decrease in housing markets.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.8
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• pp.1403-1414
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• 2010

Consumer decisions and responses about the price to pay vary. Some consumers might decide the appropriate price range prior to shopping, while others compare and evaluate prices. Especially, consumers can have different reference points for price evaluation based on various price searching behavior that represent heterogeneous responses for prices in the clothing purchase decision-making process. This research identifies how consumers evaluate the price and helps explain their decision-making based on price searches. By analyzing qualitative research, we found that consumers recalled price information as a representative indicator and product level price information through the internal search. Their level of internal references can be an important factor affecting price evaluations. In addition, each consumer groups were subdivided into high and low external searching. The four types of responses to price were classified in the price search process and the identified differences in the price evaluation. Therefore, pricing strategy needs to be differentiated for these various consumer types.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.207-223
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• 2013

Revenue management (RM) has been widely used to model products characterized as perishable. Classical RM model assumed that price is the sole factor in the model. Thus price adjustment becomes a crucial and costly factor in business. In this paper, an optimal pricing model is developed based on minimization of soft customer cost, one kind of price adjustment cost and is solved by Lagrange multiplier method. It is formed by expected discounted revenue/bid price integrating quantity-based RM and pricing-based RM. Quantity-based RM consists of two capacity models, namely, booking limit and overbooking. Booking limit, built by assuming uncertain customer arrival, decides the optimal capacity allocation for two market segments. Overbooking determines the level of accepted order exceeding capacity to anticipate probability of cancellation. Furthermore, pricing-based RM models occupancy/demand rate influenced by internal and competitor price changes. In this paper, a mathematical model based on game theoretic approach is developed for two conditions of deterministic and stochastic demand. Based on the equilibrium point, the best strategy for both hotels can be determined.

• The Journal of Society for e-Business Studies
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• v.7 no.2
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• pp.55-68
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• 2002

Internet group buying systems have been widely used recently. In those systems, because the reserve price is provided by the buyer, the success rate can be decreased if the reserve price is set too low compared with the normal price. Otherwise, an unsuitable successful bid can be made if the reserve price is set too high based on inaccurate information. Likewise, the seller's providing too high a bid price can deteriorate his/her own successful bid rate, whereas a successful bid with too low a price may make no profit in the sale. Therefore, pricing agents that recommend adequate prices based on the past buying and selling history data can be helpful. In this paper, we propose two kinds of agents. One suggests reserve prices to buyers based on the past buying history database of the system. The other recommends bid prices to a seller based on the past bidding history data of the company using the cost accounting theory. Through performance experiments, we show that the successful bid rate can increase by preventing buyers from making unreasonable reserve prices. Also, we show that, for the seller, the rate of successful bids with appropriate profits can increase. Using the pricing agents, we design and implement an XML-based group buying system.

• Journal of the Korea Fashion and Costume Design Association
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• v.12 no.4
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• pp.183-195
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• 2010

The purpose of this study is to find out the difference of evaluation of product and service quality according to consumers' attitude toward price in internet clothing purchase. This study surveyed 400 male and female consumers in their 20s-30s for empirical analysis in August 2010 who have ever purchased clothing through internet shopping malls. For statistical analysis, descriptive statistics, factor analysis, ANOVA analysis, Duncan test and cluster analysis are carried out using SPSS for Windows 12.0. The results are as follows. First, consumers' attitude toward price dimensions in internet clothing purchase are found 6 factors of sales proneness, price-prestige, price-comparison, low price, utility value and pricequality. Second, based on the attitude toward price dimensions, consumers are categorized into utility value seeking, sales price seeking, multi-dimension price seeking, lack price consciousness, low price seeking group. Third, there are significant differences in product quality and service quality depending on attitude toward price-based consumer types. In particular, sales price seeking and multi-dimension price seeking groups have higher values on product and service quality than other groups. The results of this study will help internet fashion mall businesses to develop price strategy and manage product and service quality.

• Smart Media Journal
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• v.12 no.1
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• pp.54-61
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• 2023

Pricing, as one of the most important aspects of a business, should be taken seriously. Whatever affects a company's pricing system tends to affect its profits and losses as well. Currently, many manufacturing companies fix product prices manually by members of an organization's management team. However, due to the imperfect nature of humans, an extremely low or high price may be fixed, which is detrimental to the company in either case. This paper proposes the development of a fuzzy-based price expert system (Expert Fuzzy Price (EFP)) for manufacturing companies. This system will be able to recommend appropriate prices for products in manufacturing companies based on four major pricing strategic goals, namely: Product Demand, Price Skimming, Competition Price, and Target population.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_3
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• pp.1207-1219
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• 2022

「Act On Contracts To Which The State Is A Party」 stipulates that the "Construction Standard Production Rate" and "Construction Standard Unit Price" be used as the criteria for determining the estimated price of construction works performed by public institutions. In this regard, issues such as the application scope of the Construction Standard Unit Price, and the effect of budget reduction continue. However, due to the lack of quantitative data on the actual application of Construction Standard Unit Price, it is difficult to objectively evaluate various issues. In order to prepare data for objective evaluation of the Construction Standard Unit Price, this study analyzed the ripple effect of applying the Construction Standard Unit Price based on the bill of quantity. As a result of the analysis, the Construction Standard Unit Price ripple effect in the civil engineering part was 9.2%, and it was analyzed that there was a ripple effect of about 1.9% based on the civil engineering direct cost. In the construction part, the ripple effect was analyzed to be relatively high at 17%, but it was found to have a ripple effect of about 3% in the construction direct cost. Based on the total direct cost, the ripple effect was calculated as 2.2%. Based on the analysis results, it is possible to evaluate the effect of applying the Standard Market Unit Price, and it is expected to be used as basic data to solve issues. As a future study, it is necessary to additionally analyze the ripple effect by Standard Market Unit Price application range (over 10 billion, over 20 billion won, etc.) and delivery system type (comprehensive evaluation, qualification examination, technical bidding, etc.). In addition, it is necessary to study the appropriate ripple effect of the Standard Market Unit Price.

• KIEE International Transactions on Power Engineering
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• v.5A no.2
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• pp.199-203
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• 2005

This paper proposes the level of price cap in the TWBP(Two- Way Bidding Pool) market in Korea for which the draft of market design has been prepared by KPX. Max - GMCP(Maximum Generation Market Clearing Price) and APC(Administered Price Cap) would be separately applied as individual price caps for a normal period and a Price Capping period in TWBP. The level of price cap is determined for inducing optimal investment in the Korean Electricity Market considering the 'electricity resource baseline plan' published by the Korean government in 2002 for maintaining government-leading resource planning in Korea. In this regard, Max - GMCP is calculated from the equilibrium condition of investment based on reliability standard and fixed cost of the peaking plant. For verifying the propriety of the proposed price cap, this paper compares the proposed value with the estimated VoLL(Value of Lost Load) based on Korea's GDP(Gross Domestic Product).

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.625-631
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• 2017

The stock price prediction for stock markets remains an unsolved problem. Although there have been various overtures and studies to predict the price of stocks scientifically, it is impossible to predict the future precisely. However, stock price predictions have been a subject of interest in a variety of related fields such as economics, mathematics, physics, and computer science. In this paper, we will study fluctuation patterns of stock prices and predict future trends using the Deep learning. Therefore, this study presents the three deep learning models using Tensorflow, an open source framework in which each learning model accepts different input features. We expand the previous study that used simple price data. We measured the performance of three predictive models increasing the number of priced-based input features. Through this experiment, we measured the performance change of the predictive model depending on the price-based input features. Finally, we compared and analyzed the experiment result to evaluate the impact of the price-based input features in stock price prediction.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.113-132
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• 2007

Price rigidity involves prices that do not change with the regularity predicted by standard economic theory. It is of long-standing interest for firms, industries and the economy as a whole. However, due to the difficulty of measuring price rigidity and price adjustments directly, only a few studies have attempted to provide empirical evidence for explanatory theories from Economics and Marketing. This paper proposes and validates a research model to examine different theories of price rigidity and to predict what variables can explain the observed empirical regularities and variations in price adjustment patterns of Internet-based retailers. I specify and test a model using more than 3 million daily observations on 385 books, 118 DVDs and 154 CDs, sold by 22 Internet-based retailers that were collected over a 676-day period from March 2003 to February 2005. I obtained a number of interesting findings from the estimation of our logit model. First, quality seems to play a role-I find that both price levels as proxies for store quality, and information on the quality of a product consumers have, affect online price rigidity. Second, greater competition(i.e., less industry concentration) leads to less price rigidity(i.e., more price changes) on the Internet. I also find that Internet-based sellers more frequently change the prices of popular products, and the sellers with broader product coverage change prices less frequently, which seem due to economic forces faced by these Internet-based sellers. To the best of my knowledge, this research is the first to empirically assess price rigidity patterns for multiple industries in Internet-based retailing, and attempt to explain the variation in these patterns. I found that price changes are more likely to be driven by quality, competitive and economic considerations. These results speak to both the IS and economics literatures. To the IS literature these results suggest we take economic considerations into account in more sophisticated ways. The existence and variation in price rigidity argue that simplistic assumptions about frictionless and completely flexible digital prices do not capture the richness of pricing behavior on the Internet. The quality, competitive and economic forces identified in this model suggest promising directions for future theoretical and empirical work on their role in these technologically changing markets. To the economics literature these results offer new evidence on the sources of price rigidity, which can then be incorporated into the development of models of pricing at the firm, industry and even macro-economic level of analysis. It also suggests that there is much to be learned through interdisciplinary research between the IS, economics and related business disciplines.