Dynamic Pricing and Its Forming Factors

Abstract

The determination of the proper price still remains a complex task that requires organization's knowledge not only about its operation expenditures but also about its possibilities to foresee products demand and their value with regard to a consumer. Thanks to the advance of Internet technologies and sales in electronic environment, the information about customers has become more accessible what has determined greater interest in dynamic pricing researches and their application in different services and industry sectors. This paper provides an overview of dynamic pricing concept, its terminology problems and finally the main dynamic pricing forming factors.

Full text

International Journal of Business and Social Science Vol. 3 No. 23; December 2012
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Dynamic Pricing and Its Forming Factors
Indre Deksnyte
Faculty of Economics and Management
Vytautas Magnus University
Kaunas, Lithuania
Prof. Zigmas Lydeka
Faculty of Economics and Management
Vytautas Magnus University
Kaunas, Lithuania
Abstract
The determination of the proper price still remains a complex task that requires organization’s knowledge not
only about its operation expenditures but also about its possibilities to foresee products demand and their value
with regard to a consumer. Thanks to the advance of Internet technologies and sales in electronic environment,
the information about customers has become more accessible what has determined greater interest in dynamic
pricing researches and their application in different services and industry sectors. This paper provides an
overview of dynamic pricing concept, its terminology problems and finally the main dynamic pricing forming
factors.
Key words: dynamic pricing, revenue management, e-commerce, dynamic pricing forming factors
1. The conception of dynamic pricing and its terminology problems
Looking from a historical perspective, dynamic pricing and its application became interesting research part in
1970-s when appeared research papers of Rothstein (1971) and Littlewood (1972), which analyzed the
possibilities of the application of dynamic pricing in airline and hotel sectors. When in 1978 the Airline
Deregulation Act passed by the USA congress declared that within the forthcoming four years airline sector
would not be regulated, all that encouraged the development of dynamic pricing researches in both scientific and
practical field.
Later in 1980-90-s dynamic pricing researches spread in such spheres as, car rental (Caroll, Grimes, 1995;
Geraghty, Johnson, 1997 et al.) communication (Strasser, 1996; Ciancimino et al., 1999 et al.), cruises (Ladany,
Arbel, 1991; Gallego, van Ryzin, 1994 et al.) hotels (Hayes, Miller, 2011 et al.) and finally in 1990-s – retail,
production (Subrahmanyan, Shoemaker, 1996; Bitran, Monschein 1997 et al.), telecommunications, electricity
supply (Nair, Bapna, 2001) (see figure 1).
The researches of dynamic pricing (hereafter DP) have been developed for the past 40 years; the definition has
been developing similarly. All this time DP has been analyzed and tried to be defined in various research areas:
management (Kalish, 1983; Dehbar, Oren, 1985; Braden, Oren, 1994; Desiraju, Shugan, 1999; Gallego, van
Ryzin, 1994), economics (Krugman, Phlips 1983, Baker, 1994; Grewal, Compeau, 1999; Huang, 2005;
Aguirregabiria, 1999; Zettelmeyer, Scott Morton, Silva-Risso, 2003; Chan, Hall, Rust, 2004; Sweeting, 2008;
Copeland, Dunn, Hall, 2009), operations control processes researchers (Belobaba, 1987; Williams, 1999;
Popescu, Wu, 2007; Zhang, Cooper, 2005; Ziya, Ayhan, Foley, 2004; Ahn, Gumus, Kaminsky, 2007 et al.). The
scientists conducted many empirical researches, which aimed to reveal how to model DP in different spheres of
services/industries, which factors have a significant effect on the DP formation, what is the benefit of DP
application; and finally the researches tried to define DP and its typology.

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However to define DP exactly and solidly is still a complex task for several reasons, the most important of which
are the following: different interpretation of this conception by the representatives of various scientific spheres,
the orientation of the DP researchers towards different academic branches.
One of the first and mostly cited definitions of dynamic pricing was given by the company “American Airlines”
which stated that DP is a tool to maximize revenue “selling a suitable product, to a suitable client, for a suitable
price” (Weatherford, Bodily, 1992); later this definition was supplemented with the words “in suitable time”.
Regardless of this widespread definition other researchers of dynamic pricing (Jones 1999, Weatherford, Bodily
1995) stated that there is no any competent definition of dynamic pricing which could be introduced as a standard
in research literature.
That induced constructive articles of Belobaba, which forced to include dynamic pricing into operation
management researches. However, within the context of operation management researches the understanding of
DP was restricted only to resource planning and allocation, given a certain set of prices (Desiraju, Shugan, 1999).
These researches did not estimate demand – demand profile is distinguished from both resource allocation and
company’s pricing policy. (Talluri, van Ryzin, 2004). Conversely the representatives of other spheres when
researching the principles of DP formation maintained that demand and consumer behavior are nearly concerned
with the purpose of dynamic pricing formation to control revenue (Fleischmann et al., 2004).
For a long time in scientific researches DP were identified with revenue management. So, using DP and revenue
management terms scientific literature does not contain strict boundary between conceptions that define these
terms. This range of problems also reflected in business practice of Cary (2004) who stated in his article: “...DP
and revenue management act differently in the USA airline area. DP focuses attention on rivals’ actions and the
reaction of product supply and demand. Revenue management focuses attention only on models and trends,
which are designed on the ground of demand data. Similarly Desiraju and Shugan (1999) compared DP to
revenue management and regarded them as substantially different practices. The authors of the thesis emphasizes
that these conceptions should not be identified because DP researches include the determination of optimum
product price evaluating supply/demand behavior and the assessment of that reflecting indicators.
Within a context of economic researches in DP is often related to price discrimination: DP is understood as an
attempt of a seller to force a customer to pay the highest price he is ready to spend. According to Krugman
(2000), dynamic pricing is a new practice of old price discrimination. According to him, modern technologies
made dynamic pricing useful not only for different areas of industry/services but also for economics. In this
context it is worth to mentioning Philips (1983). He summarizes a typical attitude of economist towards price
discrimination and states that DP is necessary in order to allocate resources in the optimum way in real-life
situations. This statement may sound strange because usual economic analysis states that in the competitive
market price is equal to marginal costs and that all that maximizes welfare. However, based on modern true-life
situations many sectors of industry such as pharmacy, telecommunications and information technologies
experience high fixed costs and less marginal costs. In case of such situation, when prices are set according to
marginal costs level, it would be impossible to retrieve initial investment, so in this case DP is assessed positively.
Thus, DP definition has a tendency to show which academic field governs the knowledge of this topic.
As it was mentioned at the beginning of DP researches its conception was clearly considered the part of
operational management researches (Williams, 1999). In 1999 the authors of the scientific article in the magazine
“Operational Research Society” Yeoman et al. (1999) claimed “universally accepted wide definition of DP”
sounds like “the allocation of resources and inventory to a suitable client for a suitable price in order to maximize
revenue and profitability”. Over the last years demand behavior has also been included into DP researches (Feng
and Gallego 2000), whereas the absence of standard DP definition, declared by Jones (1999) et al, allowed strain
to settle among the disciplines that research this area. Despite the influence of other disciplines operational
research still clearly dominates in the literature concerning revenue management. Although today OM researchers
recognize that product demand is an integral part of DP researches (Boyd and Bilegan 2003), the definitions are
still concentrated on supply as evidenced by Kimes and Thompson’s (2004) definition: “Dynamic pricing is the
form of resources management where supply is controlled manipulating useful life and price”. This is not
consistent to the definition of Fleischmann, Hall, Pyke (2004): “Dynamic pricing is related to price-fixing for
perishable resources taking into account demand so that to maximize revenue or profit”.

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In this context it is worth mentioning that the majority of scientists (Williams, 1999; Ziya, Ayhan, Foley, 2004;
Ahn, Gumus, Kaminsky, 2007) do not define DP in their papers; they pay more attention to DP modeling,
observation of results and try to convey DP conception showing the benefit of DP application.
Thus, the contribution to DP knowledge depended on how DP problem was defined. As consequence it would be
possible to classify DP definitions according to its treatment in different fields of science (see figure 2). In
common, definitions of DP are crisply defined as a means related to the fixing of optimal price in order to
maximize revenue. Such definitions are the most common. The second type is definitions oriented towards
demand where the greatest attention is paid to demand parameters and their contribution to DP formation. The
third type is the definitions oriented towards supply, which are the most common in the area of operation
researches. Finally, the definitions oriented towards demand/supply balance, which emphasize that price
maximization can be achieved DP controlling demand/supply disbalance.
2. Dynamic pricing forming factors
Research literature gives a wide variety of dynamic pricing forming factors. Researchers solidly agree that there is
no one solid generally accepted classification of the factors which form dynamic pricing as well as there is no
their solidly reasoned interaction. It mentions only assumptions on the ground of which it is possible to
distinguish and systematize dynamic pricing forming factors.
The researches, taking into account the multidimensionality of DP conception, it is recognized that one or several
factors do not reflect the range of problems of DP and its fully accomplished modeling possibilities, so the
researchers distinguish more and more different DP factors which can be grouped. The main factors, which are
often named DP researches, are the following. Now let’s discuss the main DP forming factors in more details.
2.1.Customer behavior and characteristics
According to Talluri, van Ryzin (2004) one of the most important factors modeling DP is the level of customer’s
knowledge. When analyzing DP scientific literature often supposes that customers are myopic customers – the
group of customers who makes a purchase provided that the suggested price is less than they want to pay. Myopic
customers do not need to accept complex buying strategy, for example, to refuse purchase in the hope of fewer
prices in future. They just buy at once when product’s price comes down lower than they want to pay (Talluri, van
Ryzin 2004). Conversely, strategic customers optimize their buying behavior responding to the pricing strategy
applied by organization. Although DP modeling is more realistic, assuming that there are strategic customers in
the market, in this case price imitates the game of strategy between an organization and a customer and aggravates
the evaluation and analysis of optimal pricing strategy (Aviv and Pazgal 2008). Thus, DP modeling with regard to
myopic customers is discussed and analyzed in scientific literature more often. It is necessary to recognize that in
most cases customers make spontaneous decisions, and all that eliminates their strategic behavior. Besides,
according to the author the lack of time and information do not allow customer to behave strategically in the
market. However, the more expensive and durable is a purchase the more important is to model the behavior of a
strategic customer (for example when purchasing luxury fashionable goods). Talluri, van Ryzin (2004) emphasize
that when modeling DP predictive models, which are supported by the statistic indicators of customers’ past, in
some specified sense reflect the influence of customer strategic behavior. According to the researches, if very
sensitive to price myopic customers tend to postpone their purchases till the end-of-season sale, the sensitivity of
these later periods to price changes will seem much bigger than it was within previous periods.
Aviv, Pazgal (2008) introduce cases when modeling product demand provided that customers are myopic, appears
the risk of “bad balance”. In accordance with authors’ example: by virtue of past statistic data the model which
operates on the ground of myopic customers confirms that it is advisable to reduce prices significantly at the end
of season because in his estimate demand is very sensitive to price during this period. However, all that can be
because customers knew that it was not worth buying at that time because prices would be reduced at the end of
the season or during holidays. If an organization has always held firm to such pricing strategy, and the customers
have been sure that this strategy will not be ignored in future, model suggestion to reduce prices is false because
the evaluation used the model made on the ground of myopic customers. However, despite all these disadvantages
the author states that DP, which is modeled on the ground of myopic customers, is widely used in practice and
gives us important and valuable information with regard to forecasting in order to maximize organization’s
revenue.

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Other important customer’s characteristic very significant for DP formation distinguished by Talluri, van Ryzin
(2004) is whether the population of potential customers is finite or endless. In case of endless population the
number of customers and their willingness to pay for a purchase are not influenced by past demand data. That is
also defined as the assumption of nondurable goods (for example, bread), when a customer who has just bought
desirables goods at once becomes the part of the population of potential customers. In case of finite population, if
one customer from the population buys goods, other purchases are expected only from the rest members of the
population. In economics theory all that is defined as the assumption of durable goods, because a customer who
purchases durable goods (for example, a car) eliminates himself from the list of the potential customers of the
population. Which of these criteria is the most suitable depends on the circumstances of DP modeling. The main
factors choosing one or another circumstance are goods type (durable or nondurable) and the number of potential
customers.
2.2. Fair prices
The majority of researchers (Campbell, 1999; Bolton et al., 2006; Haws, Bearden, 2004 et al.) detect links
between fair prices applied by organization and DP existence. Price fairness is defined as „consumer’s evaluation
and understanding whether the difference between seller’s and other party’s prices is reasonable, acceptable or
justifiable” (Maital, 2004; McFadden, 1999). The perception of price unfairness causes consumers’
dissatisfaction, the spread of negative information, which damage sellers’ reputation and encourage trust in them
(Campbell, 1999). So, in their article Bolton et al. (2006) ask researchers to pay more attention to price fairness
issues which would help to cope with negative consumers’ reactions caused by dynamic pricing. Haws, Bearden
(2004) indicated, and the author agrees with this statement, that consumers’ perception that organization’s prices
are fair in their regard is the most important condition which is necessary to hold in order dynamic pricing to
operate effectively.
In this context it is worth mentioning that in scientific literature consumers’ response to unfair prices is explained
on the ground of the following theories: distributive justice theory, equity theory and dual entitlement principle).
Distributive justice theory focuses its attention on the perception how fairly resources and rewards are allocated.
Previous researches conducted on the ground of this theory define fair justice distribution as “reward distribution
according to individual contributions to goods-money relationship” and state that people obtain the perception of
fairness when all parties involved in goods-money relationship get adequate reward. In regard to price fairness
this theory postulate that a consumer realizes price fairness when he pay the same price as other consumers for the
same product or service (Bolton, 2006).
Equity theory states that people realize the fairness (justice) of transactions comparing the ratio of their
contribution to certain transactions with results. In support of this theory researchers argue that the perception of
transactions unfairness make people feel dissatisfaction, and thus they will try to restore justice by means of their
behavior or cognitively (Haws, Bearden, 2004).
Finally, according to dual entitlement principle, Kahneman (2000) states that the perception of fairness is
determined (influenced) by orientation transactions and truck context. The parties of truck believe that they have a
right to orientation price and orientation profit. Thus, if any party does not gait its right the relationships are
understood as unfair.
2.3. Market structure
One more very important factor modeling DP is the level of competition an organization faces. The majority of
DP models are active under the conditions of monopoly when the assumption is made that product demand
depends only on itself, but not on competitors' prices. Thus, these models do not clearly take into consideration
competitive response in case of price change. DP applicability in case of monopoly is discussed in the papers of
Chatwin (2000), Feng and Xiao (2000), Gallego, van Ryzin (1994, 1997), Lin (2004), Zhao and Zheng (2000). In
case of oligopoly which estimates a response to competitors’ price changes, so DP modeling causes certain
difficulties such as models complexity and limited possibilities to collect competitor’s statistical data. However,
properly projected DP models operating under the conditions of oligopoly can provide precious knowledge
experimenting with DP application possibilities (Cachon and Zipkin (1999), Hopp and Xu (2006), Lippman and
McCardle (1997), Mahajan and van Ryzin (2001), Netessine and Rudi (2003).

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In case of perfect competition the products of each organization occupy a small share of market; each
organization sells identical goods and therefore cannot affect market prices. So, each company can basically sell
as much as it wants at a price not higher than market price, i.e. an organization has no any impact on prices.
Although perfect competition is a very important research object in economics, it is rarely analyzed in DP
researches.
2.4. Product demand
Gallego, van Ryzin (1994), Feng, Gallego (1995) emphasize the importance of demand definiteness modeling DP.
According to the author product demand is one of the most important factors forming DP. In DP researches
demand is often modeled as exogenous stochastic process with known probability distribution. However, such
models have some restrictions 1) they are fully dependent on comprehensive demand parameters while pricing
products; 2) they do not include any repeated demand evaluation devices, when in the event of the appearance of
more information that determines product demand, product prices are repeatedly reconsidered. As a consequence
in their researches scientists resolve the problems of undefined demand in increasing frequency. The literature
mentions two main sources of demand indefiniteness: indefiniteness concerning „product and consumers’
qualities" and indefiniteness concerning „unpredictable factors", such as weather. Given an unknown product
demand, in the course of time a seller tries to measure and evaluate it. In scientific literature this process is called
demand learning.
In this context it is worth to mention some important DP researches, which evaluate demand learning and its
importance for DP modeling. In their papers Carvalho, Puterman (2004), Dada, Petruzzi (2002) discuss the
problem of dynamic pricing when only the form of demand function is known, but not parameters which are
renewed in the course of time using Kalman’s filters. Aviv, Pazgal (2002) show in their works that there is
compatibility between low price which causes loss of revenue and high price which reduces purchase probability,
whereas demand is undefined for a long time. Boyd, Lobo (2003) justify the differences of market prices based on
rational learning behavior of the firms. The authors show a case of monopoly with stochastic linear demand
whose parameters are also unknown. Iyer and Bergen (2007) study the systems of „quick reaction“, in which
retailers are forced to get as much information as possible about prospective demand because of shortened time
necessary to execute an order of new products. Bitran and Wadhwa (1996) in their works also analyze the
influence of demand learning process on DP formation. The authors solve the unsteady problems of shopping and
reserve prices allocation.
2.5. The perception of product value
Customers often postpone purchases with a view to get better offer in future. However, there can be other reasons
that determine specific time of purchase. Especially when customers are not sure how they evaluate a certain
product, they make a decision to wait until they get more necessary information (Xie, Shugan 2001). During DP
researches scientists Koenigsberg, Muller, Vicassim (2006), Carvalho, Puterman (2004), Dada, Petruzzi (2002)
analyzed customer behavior in case of product value indefiniteness. Gallego, Sahin (2006) study selling options
for clients who face product value indefiniteness. Choice transaction (x, p) is originally priced by value p and that
gives it a right to require price x, after a product is evaluated. The authors give two cases of choice transactions:
(0, p1) options are of nonrecoverable price, paid before realizing value, and (p2, 0) the case – of current price,
goods are priced when a consumer identifies goods value. Yu, Kapuscinski, Ahn (2005) analyzed stochastic, but
independent cases of value definiteness. Koenigsberg, Muller, Vicassim (2006) represent the model of two
classes, two periods, where market size and composition are fixed, whereas customers face uncertainty fixing
product value. The authors additionally explore a possibility to offer last minute discount at the end of the second
period. They draw a conclusion that it is useful only when customers do not know whether these suggestions will
be offered in future.
Dada, Petruzzi (2002), Yu, Kapuscinski, Ahn (2005) do not forget to include perceptible quality into DP
researches, as an important variable directly related to product price. According to them customers use price both
as an indicator of perceptible product quality and as an indicator of perceptible costs which will be incurred
during the purchase of a product. Conducted researches allow drawing a conclusion that the perception of value is
directly related to customers’ preference and choice, i.e. the higher value perception is, the bigger a wish to buy or
product preference is.

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2.6. The Seasonality
Researchers trace that prices change and seasonal fluctuations influence some goods more than others. The sector
of style-and-fashion goods can be an example where at the end of the season old clothes collections are sold out at
a low price. Besides, in food industry some goods are sold at a discount during holidays (Chevalier et al. (2003)).
The influence of seasonality on prices can be noticed within a week, for example, discounts for alcohol drinks are
often made on Fridays. All these examples lead to the same conclusion: goods prices are reduced during the peak
of high demand. However, in their work, which is mostly related to price change frequency and extent throughout
the year, Bils and Klenow (2004) state that regardless of high demand periods it also depends on the type of
goods.
Alvarez et al. (2010) studies price changes analyzing the selling of very different types of goods within the space
of 9 years. The author made two conclusions: with regard to price changes there is no difference between durable
and nondurable goods; the bigger (smaller) degree of competition is, the bigger (smaller) price changes frequency.
According to Alvarez et al. (2010), a competition type means more than a product type as far as is concerned price
changes and the impact of seasonality on prices.
According to Warner and Barsky (1995) demand is more flexible (quickly adaptive) during the peak of demand,
what brings them to a conclusion that optimal mark-ups are anti-cyclic. When customers look for goods during
the period of high demand, they become very sensitive to price when demand is high, because they know much
more about substitute goods prices than during the periods of small demand. When customer’s price sensitivity
increases firms reduce prices holding down market segment. The research results of Chevalier et al. (2003) show
that during the periods of higher demand: prices are lower, the effect of substitutes is bigger, profitability is
smaller because prices decrease, whereas marginal costs remain unchanged; heavier expenses for the advertising
of seasonal goods.
3. Conclusion
The scientists conducted many empirical researches, which aimed to reveal how to model DP in different spheres
of services/industries, which factors have a significant effect on the DP formation, what is the benefit of DP
application; and finally the researches tried to define DP and its typology. However to define DP exactly and
solidly is still a complex task for several reasons, the most important of which are the following: different
interpretation of this conception by the representatives of various scientific spheres, the orientation of the DP
researchers towards different academic branches.
The researches, taking into account the multidimensionality of DP conception, it is recognized that one or several
factors do not reflect the range of problems of DP and its fully accomplished modeling possibilities, so the
researchers distinguish more and more different DP forming factors. These factors are the main criteria, how DP
should be modeled and adapted to the business environment.
References
Alvarez, L.J., Burriel, P., & Hernando I. (2010) Price-setting behavior in Spain: evidence from micro PPI data.
Managerial and Decision Economics, 31(2-3): 105.
Aviv, Y., & Pazgal, A. (2002). Pricing of Short Life-Cycle Products through Active Learning. Working paper.
Washington University in St. Louis.
Aviv, Y., & Pazgal, A. (2008). Optimal Pricing of Seasonal Products in the Presence of Forward-Looking
Consumers. Manufacturing and Service Operations Management, 10 (3), 339-359.
Bils, M., & Klenow , P.J. (2004). Some evidence on the importance of sticky prices. Journal
of political economy, 112(5):947.
Bitran, G., & Monschein, S. (1997). Periodic Pricing of Seasonal Product in Retailing. Management Sciences. 43,
427-443.
Bitran, G., & Wadhwa, H. (1996) A Methodology for Demand Learning with an Application to the Optimal
Pricing of Seasonal Products. Working Paper, MIT Sloan School of Management, 3898–96.
Bodily, S.E., & Weatherford, L.R. (1995). Perishable-asset Revenue Management: Generic and Multipleprice
Yield Management with Diversion. International Journal of Management Science. 23 (2) 173-185.

International Journal of Business and Social Science Vol. 3 No. 23; December 2012
219
Bolton, L. E., & Alba, J. W. (2006). Price fairness: Good and service differences and the role of vendor costs.
Journal of Consumer Research, 33(2), 258-265.
Boyd, E. A., & Bilegan, I. (2003). Revenue management and e-commerce. Management Science. 49, 1363–1386.
Cary, D. (2004). Future of Revenue Management: A view from the inside. Journal of Revenue and Pricing
Management. 3 (2) 200-203(4).
Campbell, M. C. (1999). “Why did you do that?” The important role of inferred motive in perceptions of price
fairness. Journal of Product and Brand Management, 8(2), 145-152.
Caroll W.J., & Grimes, R.C. (1995). Evolutionary Change in Product Management: Experiences in the Car Rental
Industry,” Interfaces 25, 84–104.
Carvalho, A.X., & Puterman, M.L. (2003). Dynamic pricing and learning over short time horizons. Working
paper. University of British Columbia.
Chatwin, R.E. (2000). Optimal Dynamic Pricing of Perishable Products with Stochastic Demand
and a Finite Set of Prices. Eur. J. Op. Res. 125, 149-174.
Chevalier J.A., Kashyap, A.K., & Rossi, P.E. (2003). Why Dont Prices Rise During Periods of Peak Demand?
Evidence from Scanner Data. The American Economic Review,93(1):15{37.
Ciancimino, A., Inzerillo, G., Lucidi, S., & Palagi, L. (1999). A Mathematical Programming Approach for the
Solution of the Railway Yield Management Problem. Transportation Sciences 33, 168–181.
Desiraju, R., & Shugan ,M. (1999). Strategic Service Pricing and Yield Management. Journal of
Marketing. 63 44-56.
Gallego, G., & Van Ryzin, G. (1994). Optimal Dynamic Pricing of Inventories with Stochastic Demand over
Finite Horizons. Management Sciences. 40, 999–1020.
Geraghty, M. K., & Johnson, E. (1997). Revenue Management Saves National Car Rental,” Interfaces 27, 107–
127.
Haws, K. L., & Bearden, W. O. (2006). Dynamic pricing and consumer fairness perceptions.
Journal of Consumer Research, 33(3), 304-311.
Hayes, D. K., & Miller, A. A. (2011). Revenue Management for the Hospitality Industry. Hoboken, NJ: Wiley.
Feng, Y., & Xiao, B. (2000). A Continuous-Time Yield Management Model with Multiple Prices and Reversible
Price Changes. Management Sciences. 48, 644-657.
Fleischmann, M., Hall, J. M., Pyke D. F. (2004). Smart Pricing. MIT Sloan Management Review. 45(2) 9-13.
Iyer, A.V., & Bergen, M.E. (1997). Quick response in manufacturer retailer channels. Management Sciences.
43(4) 559–570.
Jones, P. (1999). Yield Management in UK Hotels: A Systems Analysis. Journal of the Operational Research
Society. 50 (11) 1111-1119.
Koenigsberg, O., Muller E., & Vicassim, N.J. (2006). Should EasyJet order last minute deals?
Working paper.
Krugman P. (2000) What Price Fairness?, N.Y. TIMES, Oct. 4, 2000, at A35.
Ladany, S., & Arbel, A. (1991). Optimal Cruise-Liner Passenger Cabin Pricing Policy. Eur. J. Oper. Res. 55,
136–147.
Littlewood, K. (1972). Forecasting and control of passenger bookings, AGIFORS Symposia, Alliance Group of
the International Federation of Operational Research Scientists, Vol. 12, 95 117.
Lippman, S. A., & McCardle, K.F. (1997). The competitive newsboy. Operations Research 45(1) 54-65.
Lobo, M. S., Boyd S. (2003). Pricing and learning with uncertain demand.
Mahajan S., & van Ryzin G. J. (2001). Stocking retail assortments under dynamic consumer
substitution. Operations Research, 49.
Maital, S. (2004). Daniel Kahneman: on redefining rationality. Journal of Socio-Economics, 33,
1-14.
McFadden, D. (1999). Rationality for economists? Journal of Risk and Uncertainty, 19, 73-105.
Nair S. K., & Bapna, R. (2001). An Application of Yield Management for Internet Service Providers, with Suresh
Nair. Naval Research Logistics. 48 (5) 348-362.
Netessine, S., & Shumsky, R. (2004). Revenue management games. Forthcoming in Management Science.
Petruzzi, N.C., & Dada, M. (2002). Dynamic pricing and inventory control with learning. Naval Research
Logistics 49 303-325.
Rothstein, M. (1971). An Airline Overbooking Model, Transportation Science, 5, pp. 180–192.

© Centre for Promoting Ideas, USA www.ijbssnet.com
220
Subrahmanyan, S. R. (1996). Developing Optimal Pricing and Inventory Policies for Retailers Who Face
Uncertain Demand. Journal of Retailing 72, 7-30.
Talluri, K., & van Ryzin G. (2004). Revenue Management Under a General Discrete Choice Model of Consumer
Behavior. Management Science. 50 15-33.
Warner, E.J., & Barsky, R.B (1995). The timing and magnitude of retail store markdowns: evidence from
weekends and holidays. The Quarterly Journal of Economics, 110 (2): 321.
Weatherford, L.R., & Bodily, S.E. (1992). A taxonomy and research overview of perishable-asset revenue
management: Yield management, overbooking, and pricing. Operations Research 40(5): 831_844.
Williams, L. (1999). Revenue Management: Microeconomics and Business Modeling. Business Economics. 34
(2) 39-45.
Xie, J., & Shugan, S. M. (2001). Electronic Tickets, Smart Cards and Online Prepayments: When and How to
Advance Sell. Marketing Science. 20 (3) 219-243.
Xu, X., & Hoop H.W. (2006). A Monopolistic and Oligopolistic Stochastic Flow Revenue Management Model.
Operations Research. 54(6) 1098-1109.
Yeoman, I., Ingold, A., & Kimes, S. E. (1999). Yield Management: Editorial Introduction. Journal of Operational
Research Society. 50 1083-1084.
Yu, M., Kapuscinski, R., & Ahn, H.S. (2005). Advance Selling to Homogeneous Customers.Working paper.
Ziya, S., Ayhan, H., & Foley, R. D. (2004). Relationships Among Three Assumptions in Revenue Management.
Operations Research. 52 (5) 804-809.
Figure 1: Dynamic pricing development
Figure 2: The classification of dynamic pricing definition
Definition type
Features
Researchers
Appellative definitions
DP is defined as a tool maximizing
the company’s revenue and/or
profit.
Weatherford, Bodily, 1992
Huang, 2005;
Aguirregabiria, 1999;
Yeoman ir kt., 1999 and more
Focusing on demand
Definitions, which focus on
product/service demand, DP
modeled taking into account the
demand parameters.
Feng, Gallego, 2000;
Radjou, 2003;
Fleischmann, Hall, Pyke, 2004 and
more
Focusing on supply
DP is defined as resource
management tool/form.
Yeoman, 1999;
Kimes, Thompson, 2004 and more
Focusing on balance of
demand/supply
DP perceived as the best price
reflecting supply/demand balance.
Braden, Oren, 1994;
Desiraju, Shugan,1999;
Gallego, van Ryzin, 1994 and more
Retail
Production
Telecommunications
Electricity supply
Hotels
Car rental
Communications
Leisure/Travel
Airlines