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Services Strategies The Advanced Sale of Services



The practice of selling a service in advance of consumption, such as those practiced by hotels and airlines, is not equivocal amongst academics. Literature in this area is scant and there is a good deal of ambiguity as to what drives pricing and capacity allocation considerations in advance, and if advanced selling is in fact optimal. 

In investigating this phenomenon, this dissertation presents three separate and complete essays on the subject. As each essay delves into different issues and draws separate conclusions pertaining to advanced selling, each is formulated with its own introduction, literature review, some background of the study, results, discussion and references. 

In the first chapter a qualitative study of various service firms is presented. Through a theory-in-use approach, the study re-constructs the tacit knowledge of practitioners to develop a greater understanding of how service firms actually sell their capacity. In-depth interviews with the top management of service firms were performed and the qualitative data were mapped to literature resulting in seven propositions on how capacity is marketed by the firms. From the propositions, the study concludes with an analysis of the domain of the capacity strategies and the motivations that drives their usage. 

The second chapter of the dissertation investigates, through a theoretical model, the optimality of advanced selling.  In the model, the firm determines the optimal prices and capacities for advanced sale, and for the time of consumption.  To examine how demand characteristics affect advanced sale of capacities, price sensitivity is incorporated in the model formulation. The study shows that, with advanced sale, capacity utilization and profits are higher than when advance selling is not undertaken. Furthermore, when price sensitivity at the point of consumption is low, it is optimal to allocate more capacity at the time of consumption and less for advanced sale. However, although profits are greater, the optimal prices for advanced sale and at the time of consumption are lower, when price sensitivity at the point of consumption is low, than when price sensitivity is high.  

The final chapter provides a deeper understanding of demand behavior and the pricing of servicesIt argues why all services are sold in advance and show how the specificities of services result in two types of risks faced by buyers who buy in advance, that of unavailability of service and a low valuation of the service at the time of consumption. Furthermore, advanced buyers run the risk of not being able to consume at the time of consumption and this relinquished capacity may be re-sold by service firms. A theoretical model is developed that shows that advance prices are therefore always lower than spot prices. Also, providing a refund to advanced buyers may be optimal. Finally, the chapter shows a counter intuitive result that under certain conditions, the firm’s strategy may be pareto optimal in that a guarantee against capacity unavailability and a refund guarantee against valuation risk may be offered to advanced buyers at a lower advance price than if a refund offer is not provided. 



 Services are by nature perishable. As such, managing a service firm's capacity to match supply and demand has been touted as one of the key problems of services marketing and management practice. This paper advances an alternative perspective of unused service capacity. Based on a review of current literature and an exploratory study, this paper employs a theory-in-use methodology to map out a set of capacity strategy propositions. These propositions show that unused capacity could be employed as a resource to achieve a series of strategic objectives that serve to improve the performance of the firm. The paper also suggests a re-look at capacity policies and proposes that service firms should therefore approach capacity management not only from the standpoint of operations management but from that of marketing as well.

Keywords: Capacity Management, Service Strategy, Theory-in-use, Advanced Selling INTRODUCTION

Services account for a growing percentage of the gross national output of most countries. As such, service industries are maturing and have become more competitive, and there is a growing need to increase efficiency, productivity and competitiveness (Wirtz, Lee and Mattila 1998). To that end, the capacity of service firms has to be managed to achieve maximum and/or optimum utilization at all times, if possible.

Despite its importance, there has been a lack of attention devoted to the study of service capacity in the academic literature. Our field interviews indicated that managers in service firms face a complex and difficult task with regard to capacity management, and have less than adequate information to assist them. Furthermore, it is noted that there seems to be a divergence between what companies should do, according to academic literature, and what they are actually doing. This divergence seems to occur, because the literature often ignores the role of strategy when dealing with capacity issues. As competition intensifies, and despite a dearth in literature, many service firms have learned to survive by creating and innovating strategies with regard to capacity, which have not yet been explored in the academic literature. It is the purpose of this essay to map such practices, conducted in complex real world settings, and formalize the theory within which they operate through a theory-in-use methodology.

 As this investigation deals with the role of unused capacity as a strategic resource for service firms, insufficient capacity to cope with overfull demand is not addressed here. This essay begins with a literature review on capacity management that will serve to illustrate the inadequacies of academic literature in providing solutions. Following on, the method section outlines the details of the exploratory investigation of thirty-six service firms and their practices of capacity usage. These interviews with the top management of the firms, mapping of their practices, coding and categorizing through a theory-in-use methodology, generated qualitative data and, supplemented by academic literature, formed the basis for seven sets of propositions. The essay then closes with a discussion on the implications of the findings, study limitations and directions for future research.


 Capacity of a service firm is "the highest quantity of output possible in a given time period with a predefined level of staffing, facilities and equipment" (Lovelock, 1992, p. 26). Capacity amongst service firms has one commonality. For each day a service is not put to profitable use, it cannot be saved (Bateson, 1977; Thomas, 1978). This perishability suggests a need for careful planning and management, as idle capacity due to slack demand, as well as turning away customers due to insufficient capacity, are serious problems critical to the success of many service firms (Harris & Peacock , 1995). 

There is substantial literature on how to cope with supply and demand imbalances (e.g., Heskett, 1986; Lee, 1989; Lovelock, 1988; Mabert, 1986; Maturi, 1989; Morrall, 1986; Orsini and Karagozoglu, 1988; Sasser, 1976; Shemwell & Cronin, 1994). The literature suggests two ways of dealing with excess or idle capacity. The first is to manage supply to fit demand. Such strategies include reducing a firm's manpower costs, donating work to charity, conduct training for staff, schedule the service so as to match the peaks and the troughs, taking on sub-contracted jobs, and even reducing fixed costs such as renting office space or equipment. The second strategy, to manage demand to fit supply, include offering discounts, lowering prices, increasing advertising, conducting cold-calls, diversify to segments where demand is less fluctuating, selling services under barter arrangements, offering different services, positioning a service differently, accepting reservations, and even use idle staff as walking advertisements. However, if a firm chooses not to deal with the problem, it can take the option to stay with a fixed capacity that is capable of handling peak business. In sum, the current literature mainly proposes reducing/scheduling capacity, and increasing selling and promotion activities (‘chasing’ demand) or stay with a ‘level’ capacity (Sasser, 1976). Yet despite such techniques suggested by literature to alleviate the problem, capacity management remains complex and troubling in management practice and serves as one of the principal problems in services marketing (Zeithaml, Parasuraman and Berry, 1985).

Problems of Capacity Management. Typically, the objective for many service firms is to develop a capacity profile to such an extent that it matches its demand profile and yet retain its economic viability. In a perfect situation, a service firm is able to cut capacity during low season and increase capacity during peak season. However, despite an optimum choice of capacity to the extent that there may be a close fit to the demand profile, demand forecasting is a skill rather than an exact science (Dilworth, 1992). Disequilibrium from other extraneous factors may blunt such forecasts' predictive capabilities. Finding use for idle capacity in such situations would be a marketer's conundrum

 Also, not all service firms are able to fit capacity to its demand profile. This is because services can rarely achieve consistent utilization of their capacity – unless they operate through appointments (Dilworth, 1992). Even then, idle capacity may still be maintained in anticipation of potential business. When demand fluctuations are intermittent and too short in duration and there exist constraints to scaling capacity up and down, many of the proposed techniques may not be workable. This is often true with many service companies within the leisure industry, for example, hotels and car rental companies. Many of these service firms experience high fixed capacity and high perishability, and their profitability is largely tied to utilization (Allen, 1988). The result is usually a greater emphasis on short term gains and a myopic sales team, which strives to maximize yield during peak season and engaging in fierce price discounting to fill capacity during the low periods (Stone, 1990). 

 Moreover for certain services, idle capacity is required to make room for the variability of services to target to different market segments. Indeed, some estimates show that the quality of a services drops rapidly when demand exceeds even as little as 75% of the service firm's capacity (Heskett, 1986). This is because the comfort of other service users may be compromised, if the capacity of the service firm is used to the hilt.

 Likewise, other services maintain some idle capacity as the availability of the services on-demand is necessary to establish and maintain service quality and the firm's differentiation efforts (Bassett, 1992). Examples of these services would include tow truck services, lift maintenance and other emergency services, or service companies that differentiate themselves through quality and short waiting times. For such services, unavailability or waiting for the service is invariably poor service, and many firms will set capacity utilization low enough to provide near instant service, or at least, where the level of service is competitive and satisfactory against industry standards. In such cases, excess capacity is deliberately maintained to justify its employment during peak times (Bassett, 1992; Hope & Muhlemann, 1997). The question then arises as to what can be done about the excess capacity during low periods.

Capacity Management is Complex. Clearly, capacity management in services is not a simple issue. Traditionally, its dynamics has been relegated to the domain of logistics and operations management, a legacy of its counterpart in goods manufacturing. It is apparent, however, that service capacity management is far more complex. This complexity is underlined in a service firm's inability to inventory its service as opposed to goods manufacturers that are able to do so when excess capacity arises.

Furthermore, many services require the customer to be present for a service to be performed (e.g., Berry, 1987; Lovelock, 1983). Consequently, capacity management for services would affect the customer more than in the case of a manufacturing plant scaling up and down its capacity, since issues such as quality, waiting times, unavailability (i.e. marketing issues) are linked intricately to capacity. Normative research in services marketing has recognized this decentralization of the service marketing function (Gronroos, 1980 & 1991; Gummesson, 1979 & 1991; Langeard et al., 1981), which involves issues relating to operations management and planning as well.

Understanding the complexities of services capacity management is only a start. To actually propose means of handling unused capacity is a different issue altogether, which, as demonstrated above, exist in some form or another in almost all service firms. Compounding this is the fact that some service industries have relatively low entry barriers (Normann, 1984). As such, new entrants would often create an excess capacity situation in the industry, at least in the short term.  In such instances, competition often intensifies, price wars are created and profitability is compromised (Porter, 1985). Although the strategies suggested in the current literature have some virtues, the defensive undertones of such strategies suggest an inclination toward mitigating losses, and do not adequately assist companies in tackling the problems of unused capacity. Indeed, this field interviews indicate that such techniques are not frequently used in many service firms.

 The above highlights two major issues that are pressing concerns of service capacity management. First, unused capacity will exist, in some form or another. Second, techniques that are suggested by current literature are too limited to provide meaningful solutions.

 The objectives of this study are to ascertain first, how service firms deal with unused capacity in such a way so as to reduce its occurrence and its magnitude, and second, to map the actions undertaken. Such actions would then be compared against the literature and formalized into a set of propositions.


 A few exploratory interviews conducted before the study was designed indicated that many service firms handle their unused capacity in very different ways. This divergence between practice and what the literature suggests is the backdrop against the reasons why a theory-in-use methodology seems warranted.

 In their book ‘Theory Construction in Marketing: Some Thoughts on Thinking’, Zaltmann, LeMasters and Heffring (1982, p. 113) illustrate the fundamental concept of a theory-in-use approach:

                     "Practitioners……… are generally more concerned with informal theory based on everyday

observations (versus controlled experiments), having less than precise concepts (versus explicit empirical referents), and being related to one another intuitively (versus in rigorous testable relationships). The informal theory built and maintained by practitioners in their everyday activities represent an important source of insight for the researcher concerned with formal theory. By mapping these informal theories and applying their own creativity, a researcher may gain insights into marketing phenomena which might not otherwise be obtained."

 This methodology is therefore an exercise in reconstructed logic; mapping informal theory, linking with academic literature and developing a greater understanding of the phenomenon. The rationale for this approach, applied in this study, is embedded in the pursuit of knowledge[1], in the truest sense of the word. The purpose is to understand, formalize and document practitioners’ strategies in handling capacity as a contribution to academic literature.

 A case study of a cruise line first provided the authors with insights on the various innovative usage of capacity. Although a single case study would still be relevant in this methodology (Zaltmann, LeMasters and Heffring, 1982), multiple subjects were subsequently chosen as a way of discovering patterns in their strategies, how they were commonly employed, and whether such strategies exist across a diverse range of service firms. It is to be noted that this approach involves considerable interaction with the individual subjects. Hence, in-depth interviews with the top management of thirty-six service firms in Malaysia and Singapore were performed. Table 1 shows the sample composition by industry.

<Take in Table 1 >

 The managers were carefully selected to ensure that they were from the firms’ senior management and had access to relevant information with regards to the firms’ capacity usage. Care was also taken to ensure that the manager chosen had access to meetings and decisions that would offer insights to the firms' reasons for such capacity usage.  As such, the managers interviewed in the sample mostly consist of top managers, i.e. CEOs and decisions makers in the firms.   For the purpose of this essay, what is of interest is how service companies deal with their unused capacity, the actions taken by the company to alleviate the problem, and the companies’ reasons for their actions. A review of existing literature was then performed. The combination of literature and the strategies employed by the firms were then developed into a set of propositions. 


 In this section, a set of propositions on capacity related strategies is derived from findings from the study and supplemented with literature. The underlying concept of these capacity strategies seems to be the firms’ proactive intention of ensuring that a firm’s capacity is efficiently and productively utilized. The identified strategies were categorized into seven groups.  In particular, firms use service capacity for (1) customer development, (2) bundling, (3) pledging, (4) employee endowment, (5) exchanging, (6) entry deterrence, and (7) differentiation. Table 2 outlines each of the capacity strategies and the percentage of the thirty-six companies interviewed that practice these strategies. 

< Take in Table 2 >


 89% of service firms in this study use at least some of their capacity to develop customer relationships, either by (a) building loyalty, and/or by (b) providing trials. Of the 89% companies that employ customer development strategies, 81% of the firms use some of their capacity to build loyalty, and 50% use capacity to allow customers to sample their services through free trials.

Customer Development by Building Loyalty. Many loyalty programs of service

firms in this study utilize the firm’s capacity as an instrumental tool. For example, airlines give free tickets in exchange for the accumulation of miles through their frequent flier programs, and hotels give free room nights to their regular patrons. In less formal ways, many professional firms provide free advice and research to their regular clients.

 As it is discovered in this study, the use of capacity in building loyalty is perceived to be advantageous for at least four reasons. First, in some services, giving away such “freebies” may create a “snowball” effect that increases sales, as a customer who is given a loyalty-driven voucher may purchase additional services from the firm. For example, the traveler may travel with a friend or spouse, who will have to purchase another ticket. Second, giving away a service as a reward as opposed to cash invokes "images" or "mental pictures" that trigger emotional responses. A previous redemption of a loyalty-driven dinner voucher, for example, could have provided an evening of such vast enjoyment that a customer would attach a higher value to similar future rewards than mere cash.  Third, a customer may value a service according to its most visible price (for example, gross fare of an airline seat) and because the most visible price is usually a higher price, the customer feels that he has received a greater loyalty benefit (cf. Alonzo, 1996). Finally, the company attains cost efficiency from providing capacity as a reward as opposed to giving other loyalty rewards that may need to be purchased. 

Customer development through trials. Some service firms in this sample use capacity to provide potential customers with 'trials' that allow customers to experience the service before making a purchase decision. For example, the health club in this study runs programs for members to bring along a friend to try out their facilities. Such trials are always conducted carefully and closely monitored by the firms to ensure that there is no abuse. Netscape new beta version browsers, for example, could be sampled for two weeks before the software automatically expire and all its functions disabled. 

 Trials are also useful for developing channel relationships. In this study, agents selling into a new tourism product, such as a theme park, or a new destination, are often invited for free 'familiarization trips'. These familiarization trips are not merely incentives.

They serve to amplify the agent’s ability to sell the service in their home markets. One of the cruise lines in this study hosted a familiarization cruise for wholesalers and agents in five countries to develop new markets.  of course, not all services are capable of providing such trials. It would not be possible for hospitals, for example, to provide trials of their services.

Summary of capacity for customer development. The preceding discussion is summarized in the following three propositions:

  • Proposition 1a: Unused capacity can be utilized to develop customer loyalty.
  • Proposition 1b:  Unused capacity can be employed to develop new customers.
  • Proposition 1c:  Unused capacity can be employed to develop new channels of distribution.


 In this study, 83% of the firms set aside capacity to practice bundling as a strategy. Although bundling is used as a strategy for most of the service firms, this study discovered that there are different purposes why companies bundle. Our investigation isolated five purposes of bundling, and the proportion of the service companies driven by each purpose is shown in Table 2. Many firms cite multiple purposes for bundling. Before discussing each of the observed bundling strategies for utilizing capacity, a brief review on the literature for bundling is provided below.

 Background on Bundling Literature. Bundling is the parceling of two or more products, and marketing them at a discounted price. Service companies have long practiced the concept of bundling. Airline seats are bundled with tours and hotel accommodation, audit firms bundle auditing with company secretarial services, and advertising agencies bundle their creative talents with media buying services. Pure bundling is the offer of two or more services at a package price but does not provide the option of purchasing the individual services separately, i.e. in their unbundled form. Conversely, pure component selling is the selling of individual services in their unbundled state. 

 Whilst bundling strategies can serve as a powerful tool to a firm’s marketing efforts, there may be arguments against its use. Switching from pure components to bundling may result in a potential loss in revenue. Customers who wish to buy a service individually may not be so inclined to purchase the bundled services. Furthermore, customers who already have had intention of buying the bundled services as individual services will now enjoy a lower price, and the service firm would have lost the additional margin it would have earned otherwise (Guiltinan, 1997; Venkatesh & Mahajan, 1993). However, employing mixed bundling can circumvent some of these limitations. Mixed bundling provides the customer with both options, i.e. allowing them to choose whether to purchase the services in a bundle or individually (Schmalensee, 1984). Prices of services (whether sold individually or as a bundle) can be simultaneously optimized through mixed bundling in such a way that the service firm's profit can be increased over and above the expected profit than if the services were sold on a pure component basis (Adams & Yellen, 1976; Schmalensee, 1984; Venkatesh & Mahajan, 1993; Yadav & Monroe, 1993). 

 With mixed bundling providing greater revenue as compared to selling products in their pure form, a service firm can set aside some of its capacity for the purpose of bundling with other services. Our study showed that the motivation for service firms to bundle is largely due to the strategic benefits obtained from it. Such benefits include bundling for improving service performance (mentioned by 90% of all the firms that reported to bundle to utilize unused capacity; see Table 2); for increasing demand (60%); for segmenting markets (60%); for reducing a service firm's selling risk (47%); for cost reduction (37%); and for obscuring discounts (33%). 

 Bundling to provide better service value. Using part of a firm's capacity for bundling, in some cases, offers better service value and thus creates differentiation. Our study showed that an overwhelming 90% of companies that bundle, cite providing customers with better service and greater value as a strategy for bundling. 

For example, in providing customized software services, many companies bundle their customized software services with support services to ensure satisfaction from the software’s proper usage. 

For many professional service firms in this study, such as legal and engineering firms, bundling with other services is often performed, because their clients only wish to deal with one single company or person to get the job done. Hence, the professional firm, such as an engineering firm, would not only offer its’ capacity to provide a direct service to a client but would bundle some of its’ capacity with other service firms, such as soil investigation and surveying, to market itself as a 'total solution' provider. For marine and port services, bundling various services together provides for better coordination, especially over time, when increase in bundled sales fosters closer relationships between the service firms and their bundling partners. From the customers’ perspective, the availability of bundled services can sizably reduce search costs, providing them with the convenience of a ‘one-stop-shop’. 

Bundling to increase demand. Aside from providing better service value to customers, setting aside capacity for bundling was also used by 60% of firms that bundle in this study to increase demand. Through bundling, two products become a new 'bundled' product. If the two products are independent in demand, some customers who would buy only one of the products, if they were priced separately, will now buy both in the form of the bundle. The reason for this is that the value some customers place on one product is so much greater than its price that the combined value of the two products exceeds the bundled price (Guiltinan, 1987). 

 During the financial turmoil in Asia, Malaysians were willing to pay and subscribe to a bundle of 20 Cable Channels provided by ASTRO, the Malaysian Cable TV Distributor, although the demand was predominantly driven by the need for financial news information from CNN and CNBC Asia. ASTRO sold the cable channels through pure bundling and channels were not available for subscription individually. 

 Additionally, 'trade-up bundling' was used by some firms in this study, whereby various bundles are created with a slightly higher bundled price. Each higher price bundle includes an additional service and some enticement to motivate customers to 'trade-up' to the next bundle. For example, one health club in this study offered a higher discount for the purchase of a bigger bundle, comprising of facial, massage and manicure, as compared to a lower discount for the purchase of a bundle comprising of facial and massage only.

 Likewise, a customer, who is interested in buying a service from a service firm, is given a discount to purchase not merely the service itself, but a bundle of other services within the firm. This serves to increase customers' switching costs and reduce their motivation to try a service elsewhere (Eppen, Ward and Martin, 1991). As one manager in this study describes it, bundling assist the firm to get a "foot in the door so that it's easier to get the other foot in". By being a client's tax adviser, for example, an accounting firm can bundle tax consultation with regular year-end auditing services.

 Bundling to facilitate reduction in selling risk. It was found that setting aside capacity for bundling offered, in many cases, an opportunity to lower the firm's selling risk (47% of the firms that use a bundling strategy). As the threat of perishability in services is high, service firms endeavor to lower their selling risk. In this study, some service firms find that by marketing their service in a bundled form, the bundle provides the firm with a discrimination tool to lower prices and target a segmented market that is willing to buy the service in advance. Thus, bundling facilitates a firm’s attempts to reduce selling risk without cannibalizing existing pricing structures (cf. Venkatesh & Mahajan, 1993). This is the case for many airlines. Through bundling and targeting customers who are willing to purchase in advance, they are able to sell their seats in advance to tour operators. 

 Bundling to reduce marketing costs. A bundling strategy serves to reduce marketing costs and provide scope economies for 37% of firms that use bundling. As the cost structure of many service businesses is characterized by a high level of cost sharing (cf. Dearden, 1978), the marginal costs associated with marketing and selling additional services to customers are often lower in comparison to the service firm's total cost (Guiltinan, 1987). As such, some service firms set aside some of their capacity for bundling to obtain reduction in marketing costs. A hotel that sells its membership programs, which are bundled forms of hotel services such as health clubs, food and beverage and rooms, gain from lower marketing and selling costs per service when the customers buy into the program.

 Bundling to obscure discounts. In this study, 33% of the service firms that bundle use it as a strategy to obscure discounts (cf. Guiltinan, 1987).  Service firms that have low capacity utilization and are yet unwilling to engage in a price war to sell capacity may be willing to reduce their price when selling through bundles. This can be done to the extent that a customer/competitor, who tries to ascertain the precise discounts provided by the services in the bundle, end with inconclusive information. This strategy is commonly employed in multi-service companies, who are willing to impart their unused capacity to cross sell at hefty discounts to obtain a larger market share. For example, the cruise line, as a subsidiary of a major corporation, bundled cruises with a sister service firm (a hotel) with both providing discounted rates to develop an overseas market coming in on a fly-cruisestay program. The total bundled price was only marginally higher than the list price of the flight portion, which gave the perception that the cruise and the hotel were free. 

 Interestingly, in Malaysia where the airline industry is regulated and fare controls exist to the extent that penalties are imposed on airlines and agents who undersell to the market, selling airline seats in a bundle can serve to obscure discounts given to the extent that an airline can sidestep such fare control regulations. In another example, the cruise line casino in this study provided free air tickets and cruise for passengers, who were prepared to exchange a pre-determined sum of money for casino gaming chips. 

 Summary of capacity for bundling. From this study, it can be seen that setting aside a firm's capacity for bundling is a common strategy amongst service firms. Our interviews indicated that while some service firms sold their services completely through bundling, the proportion of capacity that is sold in that manner for most of the service firms ranges from 5% to 50%. The preceding discussion is summarized in the following set of propositions:

  • Proposition 2a:  Unused capacity can be used for bundling to provide better service value.
  • Proposition 2b:  Unused capacity can be used for bundling to increase demand.
  • Proposition 2c:  Unused capacity can be used for bundling to reduce a firm's selling risk.
  • Proposition 2d:  Unused capacity can be used for bundling to reduce marketing costs.
  • Proposition 2e:  Unused capacity can be used for bundling to obscure discounts.


 In this study, some service firms use capacity to endow employees and build loyalty and commitment within the company. Setting aside capacity for endowing employees is a capacity strategy, which if structured appropriately, can serve to be a powerful tool to reduce staff turnover. In examining the use of capacity for endowing employees, this study found that 64% of service firms interviewed provide free or discounted services to their employee (Table 2).

Background literature on the service employee. The quality of a service is often highly dependent on the people that deliver it. Back in the 1970s and the early 1980s, little emphasis was placed on the service employees. The mindset of service management then was to design quick and impersonal services, maximizing usage of facilities and reducing any possible individualistic intervention that could disrupt the flow of the operation. As competition intensified in the late 1980s and 1990s, a new service model emerged - that of putting service employees first, investing in, and finding ways to motivate them (Schlesinger & Heskett, 1991). These human resource strategies often increase personnel costs, although if implemented appropriately, can indeed provide high returns on investment. A firm's capacity can, through employee endowment practices, serve not only to lessen the cost burden, but also be utilized pro-actively to reduce staff turnover.

 Endowing to reduce staff turnover. 64% of service firms in this study utilize capacity to endow employees. For the larger service firms in this sample, such as the airlines, cruise lines and hotels, formal policies on staff endowment exist, and some have developed their endowment programs to highly sophisticated levels. 

 The cruise line in this study sets aside approximately 10% of its cruises for its inhouse endowment scheme, and has a discount program running within the organization. While the employee and his or her spouse can cruise for free, discount levels exist for immediate and extended family members. A lower level of discount is extended even to the employee's guests – as long as the employee cruises together with them. The management feels that this practice has brought many of the employees together and creates a better working environment. "We are happy when our staff wants to cruise. They become more involved with the product, and they become more committed towards the preservation of service quality on board," said the line's Vice President for Operations. "And when you bring your family and friends along, and your colleagues get to meet them, it's more personal and we become like one happy family – which goes a long way to building loyalty and commitment." 

 Airline endowment programs are also common within the industry. Free tickets for staff and family are perceived to contribute strongly to employee retention. Similarly, this study discovered that endowment perks, whether formal or informal, were commonly practiced for other service firms as well, from the availability of low interest loans to bank staff, free meals for restaurant staff to free post-natal programs for health club employees who are pregnant. Such endowment schemes although generally applied to unused capacity during the low periods, serve to motivate employees and are used to reduce staff turnover. 

 Summary of capacity for endowment. It is important to note that the remaining 36% of the companies do not practice using capacity for endowment primarily because these companies were performing industrial or professional services, such as port, advertising, engineering and architectural services, whereby endowing their employees with these services are not relevant. This would suggest that almost all consumer service firms use capacity to practice some form of endowment – although many of the smaller service firms admitted that such practices were ad-hoc and there were no formal policies on the matter. With committed and motivated staff, staff turnover is effectively reduced. Lower staff turnover, in turn, reduces learning curve effects, which can contribute to better service performance. The preceding discussion is summarized in the following proposition:

Proposition 3:       Unused capacity can be utilized to increase staff motivation and loyalty.


 Some service firms exchange a portion of their capacity.  In this study, 53% of service companies interviewed used their capacity to participate in some form of exchange (see Table 2). Of these, 79% exchange capacity to reduce cost, 53% to extend their existing product range, 42% to reduce selling risk, and 26% of the service firms exchange capacity to improve yield.

 Exchanging capacity for cost reduction. A substantial part of the promotional strategies for Internet giants, such as Netscape, Yahoo! and Excite, centers around the bartering of capacity on their web sites. Out of a total expenditure of $5.7m on web advertising by Netscape in 1996, it is estimated that 60% of it was on barter. Says Scott Epstein, the director of marketing communications with Excite, "No one is selling 100% of inventory. There is excess space, so why not put it to use through some sort of barter deal?" (Advertising Age, March 24, 1997, p. 26). 

 In this study, it was interesting to note that bartering capacity has taken on new levels of sophistication. Airlines barter their capacity for advertisements in magazines, promotional campaigns with travel agents, and even for entertainment expenses. For example, it is accepted practice in Asia for airlines and agent executives to socialize and conduct businesses outside the office – often on the golf course. As golfing is expensive, with a round of golf often costing US$100 per person, one airline barters tickets (a portion of its capacity on selected flights) for a pre-determined number of rounds of golf a month, which the company executives utilize to entertain agents and clients. The golf club, consequently, uses the tickets for members' inter-club competitive trips overseas or as prizes for in-house competitions - and is strictly prohibited from re-selling these tickets.

 Exchanging capacity for extending product range. In this study, 53% of the service firms that exchange capacity are motivated by product extensions. For example, in 1995 Virgin Atlantic Airways signed a code share agreement with Malaysia Airlines (MAS). Under this agreement, MAS is able to increase its number of flights on a congested route from 8 to 14 fights per week. With the increased capacity, MAS expected revenues to increase by 30% to 40%. In exchange for the right to operate the additional flights, MAS allocates a portion of its increased capacity to Virgin Airways. Although technically 11 flights are to be operated by MAS and 3 by Virgin, all 14 flights were actually operated by MAS with Virgin given an allocation of seats for each of the 14 flights. Flight numbers are shared and a Virgin Airways attendant is on board for each of the flights. By exchanging capacity for traffic rights, MAS increased its revenue and Virgin expanded its services with a new route and higher frequency at minimal costs (Aviation Week and Space Technology, 1995). 

 Similar agreements allow for other innovative capacity strategies. One example which surfaced during the course of this study revealed that by exchanging some of its capacity on a domestic route with another country's airline, the local airline is able to fly passengers from its capital into domestic points of the other country and vice versa. Thus, both airlines are able to extend their services to more destinations, even if they do not fly those routes themselves.

 Even among ocean liners, alliances to exchange capacity have expanded rapidly over the past few years to the extent that these pacts have now covered many lanes in the global shipping network (Bradley, 1995).

 Exchanging capacity for higher yield. In this study, 26% of the firms that use capacity to exchange claim to do so to facilitate yield management[2]

The concept of overbooking in this modern age of yield management is not uncommon amongst service firms, especially in the airline business. This concept entails a service firm taking in customers' bookings to a level that exceeds its capacity in anticipation of customers who fail to turn up at the last minute. In the past, such overbooking tendencies have been under great scrutiny and criticism by consumers and government to the extent that in 1978, the US Civil Aeronautics Board decreed a plan to reduce the number of passengers being 'bumped' (the act of denying a customer a seat, which he has had a reservation for). The number of 'bumps' since then have been greatly reduced due to airlines providing incentives to customers who volunteer to wait for a later flight (Simon, 1994). Hence, an airline could exchange future or alternative capacity (at a discount) to free up existing capacity for customers who are willing to pay more. This 'voluntary bumping' has served to increase efficiency in capacity utilization for the airline and greater value to passengers.

 For hotels that overbook, housing some of the overbooked guests in other nearby hotels is a commonplace strategy, as it was discovered in this study. Providing some of one hotel's capacity for the other hotel that has overbooked in exchange for a portion of the latter's capacity should the same problem arise with the former hotel assist both parties in improving their yields and gaining higher revenue. 

        Summary of exchanging capacity. Although the exchange of service capacity is not a new concept, it is surprising that little academic literature has focused on it. Notable contributions focus on bartering and acknowledge that bartering allows for much more creativity than cash deals and should be viewed as an opportunity (Vaccaro and Kassaye,

1997). In 1992, bartering for advertising amounted to $3 billion in the United States (cf. Lundberg, 1992). It is to be noted, however, that some service firms admitted to exchanging capacity as a last resort, when they were unable to receive payment for the services rendered. As this was not a deliberate strategy and so as not to confound the data, such responses were not taken in as part of the percentage of companies practicing capacity exchange strategies.

The preceding discussion is summarized in the following set of propositions.:

  • Proposition 4a:  Unused capacity can be utilized to reduce a firm’s cost.
  • Proposition 4b:  Unused capacity can be utilized to extend a firm’s product range.
  • Proposition 4c:  Unused capacity can be utilized to improve a firm's yield.


 An innovative capacity strategy of pledging was practiced by 39% of companies in this study (see Table 2). By pledging some of their capacity to channel intermediaries, service firms display their commitment to the relationship, and thereby reduce potential opportunism. Before discussing the observed pledging strategy, a brief review on the literature of distribution channels for services is provided below.

Background on Service Distribution Channels and Transaction Cost Theory. Careful analyses of the interviews conducted in this study suggest that service distribution channels are often difficult to establish for various reasons. The nature of services - their intangibility, inseparability, perishability, and heterogeneity make it difficult to relate how the "distribution" of a service can be accomplished (Light, 1986).

 Service intangibility creates problems for the marketer, as not only potential buyers are not able to see or touch the services before consumption, but channel intermediaries would also have the same problem. Thus, the role of channel intermediaries in services tend to be more complicated than their counterparts in goods distribution, as service intermediaries have to convey the idea and the quality of the service to the consumer. As Stone (1990, p. 88) puts it, "How can you rely on the agency to position your product properly, particularly when perceptions and promise are the essence of selling?" To reduce problems associated with intangibility, services channel intermediaries have to be well trained and equipped to sell the service and convey the quality of the service to its fullest degree. Hence, they would need to invest in some measure of human assets specificity, to borrow the term from transaction cost theory.

 The inseparability of many services of production and consumption may imply that direct sale is the only option and channel intermediaries may not be required. However, many services distribute a tangible representation of the service with a promise that the service will become available for consumption at some future time or date, for example, through an airline ticket, a hotel voucher or a meal coupon.

 The heterogeneity of services may result in dissatisfied customers, and thus strain the relationship between channel intermediaries and the service firm. Problems of service heterogeneity would thus rely on the strength of the relationship between channel members for their solution.

 Perishability of services also creates potential problems. In the effort to maximize yield and reduce unused capacity, many service firms practice yield management and multiple pricing. Channel intermediaries may find that the service may not be available at certain times and prices for the same service can vary, resulting in confusion and higher transaction costs for channel participants. Such problems could be solved, if channel intermediaries can be guaranteed of a service's availability at a pre-determined price, which may not be in the interest of the service supplier as he is eager to improve yields.

 It is obvious that the unique characteristics of services serve to compound the difficulties in establishing a meaningful relationship between service firms and their intermediaries.  As such, these relationships are often fraught with conflicts, which in turn, give rise to intermediary opportunism (Light, 1986; Stone, 1990). In this study, some intermediaries (travel agents, for example) attribute the lack of a credible commitment from the principal as a main cause for such conflicts. Without a credible commitment, many agents do not feel sufficiently assured to invest in assets required to build the channel relationship. On the other hand, tourism suppliers are apprehensive in giving commitment, as they believe the agent will act in an opportunistic way, once a market is established.

 Pledging Capacity to reduce opportunism. Anderson and Weitz (1992, p. 20) define pledges as "actions undertaken by channel members that demonstrate good faith and bind the channel members to the relationship. Pledges are more than simple declarations of commitments or promises to act in good faith. They are specific actions binding a channel member to a relationship."

 Consistent with literature, these studies discovered that the tourism industry has been using capacity as a form of a pledge. Tourism suppliers, such as airlines and hotels, often provide agents a portion of their capacity as a form of commitment. Such commitments, often called 'allotments', are given to specific channel intermediaries, who are able to display their commitment in return to the supplier, for example, by expending some advertising and promotion funds in the market to promote the supplier's service, or sending their staff for adequate training (human asset specificity). As an example, a hotel in

Singapore may provide a wholesaler in Japan with guaranteed allotment of rooms per night. The wholesaler, in turn, mounts a promotional program in the Japanese market for the hotel. With a guaranteed allotment (usually by way of a binding contract), the wholesaler can be assured of the supplier's commitment to his market, and the service provider gains the assurance of the wholesaler's investment. 

 Often, tourism suppliers would introduce further safeguards, such as formalization procedures[3], which would dictate the date when the capacity pledged to the wholesaler is released back to the supplier within a 'cut-off period' (i.e. the number of days before a service is to be performed). With formalization, transacting parties are more assured of each other's behavior, and such predictability fosters the relationship. Research has shown that formalization procedures reduce opportunism and increase the effectiveness of the relationship (Dahlstrom, Dwyer and Chandrashekaran, 1995). 

 This study show that for some service firms, formalization procedures dictating the cut off date when the pledged capacity is released back to the supplier, can be as long as one to two months from the actual date of service performance. Such an imposition would allow suppliers to put other potential customers on waiting lists and confirm them with the capacity released by wholesalers, who were unable to fill their allotments by the cut off dates. Consequently, even though some may argue that pledging capacity with a guarantee of sale may be too risky, wait-listing potential customers (a form of inventorying demand), who will purchase capacity released after cut off date, will substantially reduce that risk, while still giving the service firm the benefits of the pledge.

 The Asian cruise line in this study extended this concept further with a rather innovative deal involving the use of capacity. In their effort to expand their network into Australia, a marketing plan was drawn up. The total cost was estimated to be US$60,000.

This cost was split sixty-forty between the cruise line and the Australian wholesaler. However, the wholesaler was obligated to fund 100% of the amount up front and redeemed the 60% progressively over the year as sales materialized. The cruise line provided a guaranteed allotment of cabins; some form of service specificity on board the vessel specifically for the Australian market and total exclusivity to the wholesaler. Procedures for reservations were formalized with a twenty-one day cut off period so that any capacity not sold by the date was returned and sold to wait-listed customers from other market segments. The implementation of the marketing plan in Australia was monitored closely by both parties. With this deal, the cruise line was able to reduce the cost of market development, whilst the wholesaler was assured of the line's commitment to invest in the relationship. 

 Summary of capacity for pledging. Developing and maintaining distribution channels for services is a daunting task. As service industries are so diverse, their distribution channels development can differ to a large degree, and there seem to be no traditional channels to model after. The channels of distribution for a bank (for example, withdrawing money from an ATM) would differ greatly from that of a health club or an airline, although within the hospitality industry, there may be greater similarities. 

 Pledging capacity as a strategy to build channel relationships is a useful tool for many tourism service suppliers, as the sales mix for these service firms are often so geographically diverse that their limited marketing budgets tend to be ineffective.  By pledging capacity, new markets can also be developed at lower costs. The preceding discussion is summarized in the following proposition:

Proposition 5: Unused capacity can be utilized to develop new markets.


 Capacity can be used as a tool for entry deterrence. As the danger of new entrants into an industry threatens the profits of the incumbent within (Yip, 1982), the deterrence of new entrants is of paramount importance, and in fact strategies for entry deterrence often rank equal in importance as all other strategic decisions (Smiley, 1988). This study shows that 25% of service firms interviewed expand their capacity to deter new entrants.  Academic literature argues that one of the strategies available to incumbents is to build enough capacity to lower prices (Gruca and Sudharashan, 1995). Hence, whether increasing the number of staff or building a new hotel wing, the incumbent should be able to increase capacity with lower marginal costs. With an irreversible capacity choice and hence creating exit barriers for itself, the incumbent signals its capability of constantly increasing outputs to lower prices to a level, whereby it may not be commercially viable for potential new entrants to enter the market (Wenders, 1971). 

Gruca and Sudharashan also commented that using capacity to deter new entrants is most effective in industries characterized by high fixed costs, large economies of scale and high producer concentration - traits exhibited by many service industries, such as hotels and airlines (cf. Thomas, 1978). 

 In this study, one airline expanded the capacity on certain routes to ensure that other carriers do not enter the market. Although the yield on that route might have been high, the airline deliberately created excess capacity and lowered its fares to deter other airlines from flying. This strategy was also extended to maintaining idle capacity when demand was slack (for example, three weekly flights to a destination were maintained instead of reducing it to two).  In this case, the reasoning was to deter entry as well as an added benefit of retaining the flight slots (i.e. the time slot for landing allocated by the authorities at the destination), since relinquishing a time slot may allow potential competitors to take it over.

 Similarly, a Malaysian telecommunication company invested heavily into expanding their network into as many geographical areas as possible, although the markets in these geographical regions may not yet be well established. Using capacity, and pricing the service at lower rates, the firm aggressively deterred any potential new entrant from entering these markets.

 Many other people-based service businesses, such as consulting and professional firms, do not enjoy the benefit of capital as an entry deterrent. Although barriers can still be erected by way of service differentiation or developing proprietary technology (see Thomas,

1978), capacity is an alternative. An engineering firm that specializes in governmental infrastructure development claims to have a sizable share of the market. To maintain that share, the firm is careful to hire more people and yet quote at competitive prices to ensure that there is no incentive for new firms to enter and compete in that particular segment.

 Interestingly, it is also found that the motive to expand capacity is not merely to deter new entrants into the industry but to deter existing firms from entering a particular segment of the market.

The preceding discussion is summarized in the following set of propositions:

  • Proposition 6a:  Unused capacity can be utilized to deter entry into a market segment.
  • Proposition 6b:  Unused capacity can be utilized to deter entry into an industry.


 Capacity is also used as a tool to deliberately differentiate on quality. By keeping some capacity idle, customers enjoy greater comfort when consuming a service. Additionally, setting aside capacity often provides improved customer satisfaction through lower waiting times. Out of the thirty-six firms interviewed, 13% reported to deliberately keeping idle capacity for these purposes.

The rationale for keeping idle capacity for differentiation is rooted the inseparability of services, i.e. the simultaneous production and consumption of a service. For many service firms, this inseparability implies that other customers can directly affect a customer’s evaluation of a service (Bitner, Booms and Tetreault, 1990). For example, customers at a restaurant may not enjoy the service (even if the food is good), if the table next to them is making too much noise, or if their neighbors get involve in a drunken brawl. Likewise, a hotel guest may not be able to sleep well, if the nocturnal activities of the guest in the next room keep him awake all night. Whilst capacity may not eliminate such problems from occurring, it can reduce the impact of the problem. For example, the restaurant patron can be moved to another table and the hotel guest shifted to another room – subject to available capacity.

 In this study, a corporate weekly newspaper deliberately set editorial/advertisement ratio below industry norm, despite a potential loss in advertising revenue, for the purpose of maintaining less advertising clutter and thereby projecting a higher quality. The private hospital, maintains idle capacity for emergency purposes, whereas the health club keeps some 20% of capacity idle for the purpose of their members' comfort (a crowded aerobics session is a bane to members). 

 Furthermore, research shows that waiting for service negatively affects customers’ evaluation of a service (Taylor, 1994). This was evident in an interview with a golf club, where despite potential loss in revenue, golf tee off times were kept ten minutes apart (against the industry standard of eight minutes), so that golfers did not need to wait too long between shots.

The preceding discussion is summarized in the following proposition:

Proposition 7: Unused capacity can be utilized to enhance service quality.


 In the preceding sections, how service firms use capacity as a resource has been examined and the various capacity utilization strategies has been outlined.  A summary form of the capacity strategies is provided in Figure 1. 

The findings indicated that whilst the capacity strategies of Customer Development and Employee Endowment, Entry Deterrence and Differentiation often use unused capacity, the capacity strategies of Bundling, Exchanging and Pledging are not directly applied on unused capacity. The firms, in fact, set aside a certain portion of their capacity in advance to employ these strategies. This is, in part, due to the benefits that can be obtained from the strategies but also due to a difference in time. As the firms may not be able to predict how much of their capacity will be unused at a certain future time, they preempt such possibility by committing a portion their capacity in advance through the employment of these strategies. This need for insurance due to the perishable nature of services, drive the capacity strategies just as it drives the concepts of reservations and overbooking.  It is interesting to note that what originally started as a study on service firms and how they handled their unused capacity, seems to have obtained quite unexpected results. Along the course of this study, it has been discovered that many firms take a different approach towards capacity management, versus what some of the literature have suggested, namely, increasing selling activities and reducing capacity costs. 

Specifically from the point of view of managing demand to match supply, many companies do not wish to pursue active discounting or increasing advertising and promotion activities to promote capacity utilization. Such moves are feared to impact negatively on marketing plans that have already been set in place. However, some service firms that are able to accept advance sales, such as airlines and hotels, do practice reservations to diminish the impact of unused capacity, often in tandem with the capacity strategies.

 From the point of view of managing supply to fit demand, this findings indicate that the scheduling of services was not a way to curb unused capacity but merely to mitigate losses. Even then, many firms admitted that the amounts saved are small as compared to the actual loss of income derived from the capacity that is unused. Hence, it would seem that firms are generally more keen in setting a ‘level capacity’ whilst employing the capacity strategies to strategically consume the unused capacity. 

 It can therefore be seen that the focus of these firms where capacity is concerned, is to act proactively to preempt and reduce the occurrence and magnitude of unused capacity. Hence this study suggests a divergence between academic literature and practice in so far as the focus and approach towards capacity management is concerned. In light of this, there is a need to shift current academic emphasis on capacity management and address the issue in a more proactive manner (cf. Bassett, 1992).

 The striking factor in this study is that these proactive strategies utilize capacity itself as a strategic resource to improve business performance. This seems particularly advantageous for small and medium service enterprises, where resource constraints are a major obstacle in developing their businesses (cf. Donald et al., 1991; Lee, Wee and Chia, 1997). 

A Possible Domain for Capacity Strategies. From the findings and the propositions developed in this essay, it is interesting to discover the underlying reasons behind the use of these capacity strategies. What are the reasons behind the changing of a firm’s perspective about capacity to the extent that solutions from literature is less practiced and capacity strategies thrive in its stead? Do these benefits provide a better outcome as opposed to suggestions from literature? 

 While this essay does not address the above questions, a few are advanced as arguments and as a possible justification for the domain under which these strategies would prevail and the contextual nature of their existence. 

Since capacity strategies are not known to be commonly practiced by goods marketing firms (with entry deterrence being an exception), the answer to the first question may possibly rest in the unique characteristics of services.

 First, the perishable nature of services has various implications on capacity. For a service firm that has a capacity to perform a certain level of output on a particular date, unused capacity cannot be stored for use on a later date. In other words, services have no salvage value. This perishable nature of services drives service firms toward a greater urgency to maximize sales forward. This sense of urgency may therefore be a driving force to some of the proposed capacity strategies.

 Second, the intangible and inseparable natures of service are also likely to impact on capacity strategies. Intangibility and inseparability could possibly drive service firms toward a greater need to reduce perceive risk of purchase both by customers and by channel intermediaries. This need may also be another driving force for some of the proposed capacity strategies.  If service firms are driven by the above two needs, it can then be further argued that these needs change a service firm’s perspective of capacity. In other words, the objective for a service firm is no longer to match demand and supply, but to maximize forward sales and reduce perceived risk. To that end, the strategies employed are aimed to achieve these objectives, instead of merely matching supply and demand. With this shift in focus, it is understandable why service firms would be more inclined to employ capacity strategies instead of the techniques generally suggested in the academic literature. 

To summarize, this essay proposes that, except for the use of capacity for entry deterrence which is practiced also by goods firms, the three unique characteristics of services (i.e. perishability, inseparability and intangibility) push the firm towards the strategic objectives of maximizing forward sales  (i.e. by practicing advanced selling) and reducing risk of purchase.  With these strategic objectives, service firms would then employ the capacity strategies which would then lead to the strategic benefits of increase sales and improved profitability and which, in turn, lead to the outcome of improved business performance.  A skeletal model amalgamating the above arguments is illustrated in Figure 2.  It is hoped that this study would stimulate research to develop this model further.

 Other Research Issues. The findings also suggested that operation policies and yield management practices often hinder the employment of capacity strategies. Many of the operations departments within the service firms interviewed treat capacity strategies as a necessary burden and a bane for depriving the company of more sales, as they tend to inhibit yield management practices. As such, marketing and sales teams constantly cross swords with operations and yield management teams in so far as capacity utilization is concerned, especially during peak seasons. The marketing and sales departments consider the practicing capacity strategies necessary, even during peak seasons, to ensure long-term revenue and to a large extent, assist in filling up capacity during the low seasons. The operations departments, however, are concerned with short-term yields and would constantly attempt to fill up capacity at high rates with little concern for the marketing implications. In several interviews, it was discovered that this conflict between short-term yield and long term revenue seems to be a constant battle within many service firms. 

 Such a conflict provides an avenue to investigate further the cost of unused capacity. Being able to cost unused capacity, whether in terms of actual cost or opportunities lost, can be a starting point towards evaluating potential pay-offs obtained from employing the various capacity strategies proposed. However, research acknowledges that the guidelines and policies towards accounting and measuring unused capacity are still vague (Brausch & Taylor, 1997; McNair, 1994). Without the ability to cost unused capacity, and thereby assessing capacity strategy pay offs, conflicts between short-term perspectives and long run increases in revenue will continually persist. It is hoped that future research shall also delve deeper into this area.

 Besides the managerial implications from this presentation of capacity strategies, the theoretical aspects suggest a host of future research possibilities. Within each capacity strategy, more empirical research is required, which would serve to develop a better understanding of its usage.

 Finally, this essay does not address the issue of under capacity, i.e. when demand outstrips capacity. The predominant emphasis of this essay is the use of unused capacity as a strategic resource.  As such, should the happy problem of excessive demand and insufficient capacity be encountered, there is no unused capacity to speak of, and thus, no such resource is available.

 A major contribution of this essay is the advancement of an alternative and proactive view of a service firm’s capacity as a strategic resource, through a theory-in-use methodology, manifested onto seven propositions. It is my belief that this contribution provides pedagogical benefits and substantial relevance to the practice of services management and marketing thinking.


 A service product is unique because of its intangibility, perishability, inseparability of production and consumption, and heterogeneity (cf. Berry & Parasuraman, 1991). These unique characteristics pose special challenges to service marketers, and therefore require them to be more creative in developing marketing strategies (Reddy, Buskirk and Kaicker, 1993). With strategic planning and the development of competitive strategies playing a more important role in service companies (Wilson, 1988), a company's resources have to be managed so as to maximize its revenue according to the fluctuations in demand.

 Service firms should view their capacity as an opportunity to formulate strategic plans and to exploit this opportunity to achieve maximum gains.As noted earlier, capacity management is often thought of as an operations management issue. Such delineation can be counter-productive, as the development of a firm's core competence is in its ability to work across functional organizational boundaries (Prahalad & Hamel, 1990). By working across functional boundaries, service companies can then boost efficiency and productivity and remain competitive. As an added emphasis and to aptly depict the essence of this essay, I conclude with the words of Ohmae (1982, p. 39): 

 "…Even when the company has in effect no more management resources than its competitors in the same business or trade, it can often achieve resounding competitive success, if it is effective in bringing these resources to bear on one crucial point."

 Hence, the crucial point for competitive success in services hinges on efficient capacity utilization, and that the use of capacity, as the service firm's invisible but powerful resource, will bear on that point.


Table 1: Companies interviewed by industry


No. of Companies



Consulting Firms





IT Firms (Software Programming)



Law Firms



Marine Services



Advertising Agency



Architectural Firm



Engineering Firm



Hospitality  Cruise Lines

















Travel Agent



Leisure  Golf Club





Health Club



Movie Cable Network



Stage Entertainment Firm



Media & Telecommunications

Media (News Service)









Retail Banks





Finance Company



Private Bank




Higher Education Colleges





Total Sample



Table 2: Capacity Utilization Strategies employed by the sample firms (Multiple Responses)

Capacity Utilization Strategies

No.               of





% of Total 

Customer Development

Companies that use capacity: To build loyalty










To provide trials











Companies that bundle:

To provide better service value











To increase demand





To reduce selling risk (insurance)





To reduce marketing costs





To obscure discounts










Employee Endowment

Companies that use capacity for employee endowment









Companies that exchange: To reduce cost











To extend product range





To improve yield











Companies that use capacity for pledging








Entry Deterrence

Companies that use capacity for deterring entry









Companies that use capacity for differentiation















Unused Service Capacity Can be Used for : 









By building loyalty (P1a)

By providing trials (P1b)

To develop new

channels (P1c)





To    provide      better

service value (P2a)

To increase demand (P2b)

To reduce selling risk (P2c)

To reduce marketing costs (P2d)

To obscure discounts







To          increase

staff motivation 

and loyalty (P3)






To reduce cost (P4a) To extend product range (P4b)

To    improve      yield






To develop New Markets








Through capacity expansion in a market segment  (P6a) or in an

industry (P6b)






To enhance service

quality (P7)



Note:  Proposition numbers developed  in the essay are provided in parentheses42


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 Should service firms sell in advance?  Using a theoretical approach, I study the optimality of advanced sale of capacity for a monopolistic service firm and examine the impact of market price sensitivity on the optimal price and capacity allocations for advanced sale.  I show that, when firms undertake advanced sale, capacity utilization and profits are higher even though prices for sale in advance are discounted.  In addition, I show that optimal pricing and capacity allocations for advanced sale depend on the expected price sensitivity at the time of consumption.  When price sensitivity at the point of consumption is expected to be low (unchanged), it is optimal to allocate more (less) capacity for sale at time of consumption than in advance.  Although optimal price and capacity allocations in advanced sale result in excess capacity, I show that having excess capacity is a strategic decision in that it is dominant strategy.

Keywords: Capacity Management, Advanced Selling, Pricing, Yield Management.


 In 1991, when there was a slump in the hotel industry, Marriott Corp. offered non-refundable discounted room rates for customers who were willing to purchase in advance.  This resulted in the firm selling in advance 250,000 non-refundable room nights, which was twice that expected (Weissenstein, 1991).  However, this practice did not catch on in the US, as the industry in general took a negative view of it.

 More recently, with a drastic drop in tourism due to the economic crisis, the practice of advanced sale has resurfaced in Asia.  In November 1998, Siam City Hotel Bangkok held an auction to sell off blocks of room nights available for the rest of 1998.  It succeeded in selling 1,400 room nights at an average price of 700 baht per room, which was much lower than standard quoted rates at that time (Hambi, 1998).  In a similar move, Sheraton Towers in Kuala Lumpur sold blocks of 1999 room nights in November 1998 to major corporations in Kuala Lumpur, at a much discounted rate.  The practice of advance selling is not restricted to the hotel industry, it is also known to be done in industries like the airline and the car rental industries.

 These industry cases of advanced sale raise several issues.  Given that not all in the industry have a positive view of the advanced sale of capacity at a discounted rate, it is uncertain if such a practice is in fact optimal.  Although studies have suggested that advance sales can be a profitable yield management practice (Nagle and Holden, 1995), little formal research has been performed to examine the optimality of advance sales.  In addition, given that the occurrence of these practices coincides with an economic down turn, it raises a strong possibility that demand conditions affect the practice of advance sales.  However, it is not clear how demand conditions impact advanced sale, in terms of optimal pricing and capacity allocations. This essay therefore attempts to resolve some of these issues to gain some insights into the practice of advanced sale. 

 It is acknowledged in the academic literature that the characteristics of services, in terms of their perishability and inseparability, require the management of demand and supply such that they match at any given point in time.  This is because unused service capacities cannot be inventoried, nor can they be readily altered in advance.  One way that service firms can achieve such a demand-supply match, is to sell in advance their service capacities.  However, despite the practice of advanced sale of capacities by industry, theoretical research on advance selling is scant.  As such, issues about the optimality of advanced sale of service capacities, and optimal pricing and capacity allocation of advance sales, have not been formally investigated.

 This essay examines the optimality of advanced sale of service capacities from an economic perspective.  A game theoretic model of advanced sale of service capacities is presented, for the case of a service firm commanding a monopolistic position.  This means that the firm has some degree of market power as a result of a its differentiation efforts, at least in the short term (Chamberlin, 1933).  Service firms achieve a monopolistic position through appropriate positioning and differentiation strategies to build brand equity, and implementing promotions strategy like customer loyalty programs, all of which increase customer switching costs (Reichheld, 1996).  Hence, in practice, it is not unusual that service firms can command monopolistic positions even though they are not the only service providers in the market.

 In this model, the firm determines the optimal prices and capacities for advanced sale, and for the day of consumption.  To examine how demand characteristics affect advanced sale of capacities, price sensitivity is incorporated in the model formulation, and the study investigates its impact on a firm's optimal pricing of, and capacity allocation for, advanced sale.

 The study shows that, with advanced sale, capacity utilization and profits are higher than when advance sales is not undertaken.  However, profit maximization necessitates that firms tolerate excess capacity.  This is because the profit maximizing price level need not be the same as that which maximizes sales of capacity.  In fact the study shows that having excess capacity is a dominant strategy in terms of profit maximization, across the varying price sensitivity conditions examined.  In addition, it shows that, when price sensitivity at the point of consumption is low, it is optimal to allocate more capacity at the time of consumption and less for advanced sale.  Doing so also results in better capacity utilization.

 However, a counter intuitive result is that, although profits are greater, the optimal prices for advanced sale and at the time of consumption are lower, when price sensitivity at the point of consumption is low, than when price sensitivity is high.  It is also rather surprising that the optimal price discount for advanced sale is higher when price sensitivity at the time of consumption is low.  These suggest that firms should capitalize on the opportunity to increase capacity utilization when price sensitivity is low, even if it means a lowering of prices.


What induces firms to sell their service in advance?  Several inferences can be made based on industry practices.  In the case of Eurotunnel (the operator of the Channel Tunnel), advance selling was done for cash flow reasons, to maximize cash revenues so as to ease the burden of paying interests on huge loans (Anon, 1994).  In the TV syndication industry, lower ratings that resulted from an oversupply of action/adventure cartoons prompted TV syndicates to advance sell in order to preempt competition, for an alternative softer animated series in 1987.  In the trade show industry, advanced sale is conducted to ease planning, and for efficiency reasons

(Pridmore, 1987).  For the same reason, advanced sale (or “upfront” selling, as it is termed in the industry) is also undertaken in the advertising industry.  For example, by advanced selling, the early monetary commitments by advertisers allows TV networks to plan their promotions budget based on committed revenues, to ensure that their shows achieve the promised ratings (Walley, 1990).  However, despite the practice in some industries to advance sell, the optimality of undertaking advanced sale is not equivocal among industry members.

Current literature suggests that perishability may be a reason that service firms practice advanced sale of capacities.  Unused capacity after the point of consumption/production has no salvage value.  Thus, the perishable nature of services "drives service firms toward a greater urgency to maximize sales forward" (Ng, Wirtz and Lee, 1999).

In addition, it has been suggested that price discrimination over time may be a driving force for advanced sale (Png, 1991).  This is plausible because, the uncertainty of service availability at the point of consumption may make a segment of consumers, who are more risk averse, to purchase in advance as an insurance against uncertainty (Png, 1989).  Knowing this, firms can then maximize their profits by selling in advance, charging a price premium for advanced sale, and discounting their prices for remaining capacities as the consumption date approaches (Png, 1991).  However, this does not explain the practice of price discounting in advanced sale.

With the exception of Png’s studies (1989, 1991), there is little theoretical research being conducted on the topic of advanced sale.  Like Png, this study examines the optimality of advanced sale of service capacities from an economic perspective.  Similarly also, a theoretical approach was adopted to examine advanced sale of service capacities for the case of a service firm commanding a monopolistic position.

Other than the similarities described, this model differs from that of Png’s in significant ways.  In this model, both pricing and capacity allocation are examined for advanced sale.  To do this, the study structures this formulation based on a Cournot type response function, in which the firm chooses capacity allocations for sale in advance vis-à-vis at time of consumption.  In addition, price sensitivity is incorporated in this model, which allows us to examine its impact on the firm's decision on pricing and capacity allocations.  Finally, Png (1991) assumes demand outstrips capacity, hence the issue of excess capacity does not arise.  In contrast, this study does not place such a restriction on demand, as it is also interested in examining the impact of advanced sale on capacity utilization. 

The issue of advanced sale of capacities differs from that of price discrimination to influence demand such that a demand-supply match is achieved (e.g. the use of peak load pricing), which has been well discussed in the literature (Nagle & Holden, 1995).  The latter involves pricing across different times of consumption, whereas advanced sale involves pricing for purchases made in advance of consumption at a particular time.  For example, a hotel room on the Friday night of a long weekend is greatly sought after, compared to the same hotel room the day before.  This suggests a higher pricing for the room for Friday night, than other days.  However, in advanced sale, the study addresses the issue of how to price the same room for a particular day (Friday, for example), for consumers who wish to purchase well in advance of that day. 

The rest of the essay is organized as follows.  Model formulation is presented in §3.  In §4, the analyses are presented.  The study first considers the case when the firm does not practice advanced sale (§4a), to provide a benchmark for comparison later.  This is followed by the case in §4b, in which the firm practices advanced sale of capacities.  Finally, in §4c, the impact of price sensitivity on pricing and capacity allocations is examined, when the firm practices advanced sale.  §5 discusses the results, and the final section contains some concluding remarks with regards to managerial implications and directions for future research.


Consider a service firm, for example a major hotel, which has achieved a monopolistic position as a result of its differentiation efforts, and has a total room capacity for any particular day of K.  Since capacity cannot be easily altered, it is assumed that the capacity K is a constant.  When deciding on advanced sale, the firm has to decide on the price and capacity to allocate for purchases made in advance.  Let t be the time to the consumption date, t0 (when t = 0) be the time of production/consumption, and tA (such that tA > t0 = 0) be the time of advanced sale.  At t0, the service is produced and consumed, and any unused capacity that arises has no salvage value after t0.  it is assumed that tA and t0 are exogenous.

In practice, tA is industry specific.  For example, in the hotel and airline industries, firms are known to sell their capacities more than a year in advance in some markets (hence, tA is more than a year).  Whilst in the advertising industry, TV networks are known to sell their advertising spaces approximately 6 months in advance (tA representing 6 months in advance).  Hence, tA is defined as the time in advance of the date of service consumption that clients are prepared to make a purchase commitment.  This also includes all forms of advanced bookings and reservations for a service, which is binding.

As service firms in general operate with high fixed costs, C, which is much higher than the variable cost of capacity, the study considers the case when variable costs are sufficiently small to be ignored, for example airlines and hotel.  Let k0 and P0 be the capacity and price of a unit capacity, respectively, at the time of consumption t0.  Similarly, let kA and PA be the capacity and price of a unit capacity, respectively, at the time of advanced sale tA.  Hence, the firm’s objective function can be defined as, Max.k π = Max.k [(PAkA  + P0k0) – C]

Where, π represents the firm’s profits.  The objective function implies that at a time before tA, the firm chooses price and capacity allocation for t0 and tA, to maximize profits.  Let the total sales revenue due to the firm be R, where R = Pk. I assume that the firm’s objective is to maximize profit although I acknowledge that capacity maximization may not purely be for profit maximization, but instead, be for other managerial focused objectives.

It is assumed that price is a linear decreasing function of total capacity available at any point of time t.

P = α - β(k), where k = total capacity at time t

Hence, at time tA, the demand function becomes, 

PA = α - β(kA + k0)  (1)

While at time t0, the demand function is,

P0 = α - β(k0)   (2)

The use of a linear demand function is consistent with prior marketing research. 

The use of linear demand functions is extensive in theoretical research (eg. Jeuland and Shugan, 1983; Ingene and Parry, 1995), and in empirical studies (Lilien, Kotler and Moorthy, 1992).  This is because a linear demand function can be a reasonable approximation of a non-linear one, given that the latter can be defined as one that comprises a series of linear functions (f(x) = ∑i fi(x): xj ≤ x ≤ xk, i = 1 to n, j ≠ k) over an appropriately partitioned (xj ≤ x ≤ xk) non-linear one such that the range of each partition approaches zero (ie. (xk - xj) → 0).  The accuracy of a linear function as an approximation of a non-linear one thus depends on how the non-linear function is partitioned and the range of interest. Furthermore, our demand functions assume that the price at spot isn’t affected by advanced prices. In practice, this is seen with airlines and hotels where advanced tickets can only be purchased with several conditions (termed as ‘fare rules’), and at a specific time. While this assumption is not unreasonable, I acknowledge that is a limitation of the model and relax this assumption in following chapter.


The study first analyse when the firm does not practice advanced sale, before considering the case when the firm practices advanced sale.  All proofs to the propositions are found in the appendix.

(a) Profitability without Advanced Sale

           Without advance sales, the firm's objective function becomes,

Max.k π = Max.k (P0k0 – C)

 Substituting equation (2) for the price function, into the firm’s objective function, and deriving the first order condition with respect to k0, the optimal capacity allocation can be solved.  The solution being, 

k0 = k* = α/2β

However, this assumes that an interior solution exist, that is k0 = k* ≤ K.  In which case, given that k*= k0 = α/2β, the optimal price and resulting profits would be,  P0 = P* = α - β(k*) = α/2 π = (P0k0 – C)  =  [(α2/4β) – C]

This also results in the firm having an excess capacity, 

e = (K – k*) = (K - α/2β) > 0

However, if k0 = k* > K, then k* = K, resulting in an excess capacity e = 0.  Under this situation, the optimal price and the resulting profits would be, P* = [α - β(K)] and π*  = [(α - βK)K – C], respectively.

Proposition 1: Having excess capacity intentionally is profit maximizing if K > α/2β.

 The proposition is rather counter intuitive, as a common call in the service literature on capacity management calls for matching demand and supply, thus implying that excess capacity is unhealthy profit wise (Orsini and Karagozoglu, 1988; Sasser, 1976; Shemwell & Cronin, 1994).  However, the proposition is intuitively plausible, as a low price level that maximises the sales of capacity causes a firm to lose out in profit margins.  The loss in profit margins can be sufficiently large, to the extent that a low price that maximises capacity utilisation may not be profit maximising.  Furthermore, if the variable cost of capacity is sufficiently low, then excess capacity does not pose as a substantial cost burden.

 For the rest of this essay, the study focuses only on the case when an interior solution exists – that is k* ≤ K.  Otherwise, the results will be driven strictly by boundary conditions, and maximum achievable profits are not attained.

(b) Profitability with Advanced Sale

If the firm chooses to sell in advance, its objective function is,

π = [(PAkA  + P0k0) – C]

Substituting equations (1) and (2) into the above, and deriving the first order condition with respect to kA and k0, the optimal capacity allocations is derived after solving the first order conditions simultaneously.

k0  = k*  = α/3β kA  = k**  = α/3β

e = (K - k* - k**) = (K - α/3β - α/3β) = (K - 2α/3β)

 Substituting k* and k** into equations (1) and (2), and rearranging, the optimal price levels can be derived, which are as follows.

P0 = P* = 2α/3

PA = P**= α/3 

Proposition 2a:  Capacity utilisation is higher when advanced sale of capacity is undertaken, than

when it is not, i.e. e(advanced sale) < e(no advanced sale).

Proposition 2b:  Advanced sale of capacity at a reduced price of PA = α/3 supports an even higher

price at time of consumption of P0 = 2PA = 2α/3, than when no advanced sale is undertaken (P0 = α/2). 

 When no advanced sale is undertaken, then a larger amount of capacity available at the point of consumption (k0 = k* = α/2β) drives prices down.  However, with advanced sale, half the capacity is sold in advance (albeit at a lower price), resulting in less capacity being available at the time of consumption (k0 = k* = α/3β = kA = k**).  Hence, prices are driven up at the time of consumption.  The overall effect is that capacity utilisation is improved, and profits are increased, as Proposition 3 below shows.

 This perhaps provides an explanation for the practice of advance selling of TV advertising slots in the USA, as reported earlier.  TV networks typically sell 75%-80% of their season’s advertisement capacity months before the advertisements are aired.  The remaining capacity is held back for quarterly “scatter” markets, where prices are much higher because there is less capacity available to meet demand (Mandese, 1995). Substituting the above prices in the firm’s profit function, the following is obtained after some algebraic rearrangement,

π = (α2/3β) – C

Proposition 3a:  A symmetric allocation of capacity across t0 and tA, k* = k** = (α/3β), is optimal.

Proposition 3b:  Advanced sale of capacity, even at a price discount, is more profitable than when no advance sale is undertaken: π((P0, k0), (PA, kA)) > π(P0, k0).

 Allocating half of a firm's capacity for sale in advance is optimal in terms of profit maximisation, and is more profitable than selling all the capacity at the time of consumption.  The result, that having advanced sales is more profitable than having no advance sales, is consistent with that obtained by Png (1991).  However, Png’s study does not address the issue of capacity allocation for advanced sale.  In addition, in Png’s analysis, advanced sale is carried out at a price premium.  In contrast, a price discount is optimal, in this analysis.

 Proposition 3 is consistent with the current industry practice of advanced sale of airline tickets in Asia.  Customers purchasing airline tickets in advance will find ready availability of seats, and at a significantly reduced special fare - commonly termed APEX (Advance Purchase Excursion) fares.  However, passengers who purchase their tickets close to the departure date, will find to their dismay that seats are not readily available, even if they are prepared to pay a price premium. 

(c) Low Price Sensitivity Forecasted at t0

 Given that economic conditions can change over time, it will affect price sensitivity accordingly.  Hence, in this section, the study investigates how a low (or a high) price sensitivity forecast can affect advanced sale.  When consumers have low price sensitivity at t0, then they are less sensitive to price variations in making their purchases at t0.  Hence, an additional unit of capacity allocated at t0 results in a smaller decrease in price at t0.  To incorporate changes in price sensitivity over time into this formulation, let the price sensitivity at t0 and tA be β0 and βA, respectively, such that,  

β0 ∈ {βL, βH} and βL < βA < βH

 The symbol βL represents a price sensitivity that is low, while the symbol βH represents a price sensitivity that is high, at t0.  Incorporating the differing price sensitivities into the firm’s objective function, the following is obtained, 

= (α - βA(kA + k0))kA + (α - βL(k0))k0 - C

By deriving the first order condition with respect to kA and k0, and solving the resulting equations simultaneously, the optimal capacity allocations are derived, which are,  

k0  = k*      = α/(4βL - βA) kA          = k**  = [α(2βL - βA)]/[(4βL - βAA]

Proposition 4a:  When price sensitivity at t0 is expected to be low, more capacity is allocated for sale at time of consumption t0, and less capacity is allocated for advanced sale at tA, resulting in k*/k** > 1.

Proposition 4b:  When price sensitivity at t0 is expected to be low (β0 = βL  < βA), capacity utilisation is greater than that when (β0 = β  = βA), resulting in lower excess capacity, if βA > βL > (βA/4).

 A decrease in price sensitivity as the consumption date approaches can be due to imminent perishability of the service.  The fact that a service is perishable is doubleedged.  Neither a firm nor a customer can store capacity.  Hence, when consumers are uncertain about the value they place on the service till close to the time of consumption (Png, 1989), they may find themselves with less time to make purchases and less choices as well.  These translate into a lowering of price sensitivity being observed in the market, as the date of consumption approaches.

 In addition, a consumer who has not made any advanced booking for a hotel is likely to be less price sensitive when he/she requires a room at the time of consumption, than one who has made an advanced booking well ahead of the time of consumption.  With regards to the hotel industry, it is a known fact that business travellers, who usually make their hotel bookings close to the time of consumption, are far less price sensitive than tourists, who usually make their hotel bookings far in advance.

 Given that price sensitivity is lower at the time of consumption, a firm can therefore capitalize on this by allocating a higher capacity at t0, and less at tA.  Doing so will also increase capacity utilization, resulting in lower excess capacity.

Substituting k* and k** into the price and the firm’s profit functions, the following is obtained,

                                              P0         = P*    = α(3βL - βA)/(4βL - βA)

                                              PA        = P**   = α(2βL - βA)]/(4βL - βA)

= {[α(3βL - βA)/(4βL - βA)][α/(4βL - βA)]} + {[α(2βL - βA)]/(4βL - βA)][α(2βL - βA)]/[(4βL - βAA]} – C

⇒        π        = [α2βL/((4βL - βAA)] – C


Proposition 5a:  The price levels (PA, P0), when price sensitivity at t0 is low (β0 = βL  < βA), are

lower than that when (β0 = β  = βA).

Proposition 5b:  When price sensitivity at t0 is low (β0 = βL  < βA), the price reduction (PA/P0)

for advanced sale is greater than that when (β0 = β  = βA).

Proposition 5c:  When price sensitivity at t0 is low (β0 = βL  < βA), profitability is greater than

that when (β0 = β  = βA), if βA > βL > (βA/4).

 A rather surprising result is that the optimal price levels for advance sales and at the time of consumption are reduced (Proposition 5a), when the market is forecasted to be less price sensitive at the time of consumption.  It is also counter intuitive that the optimal price discount for advanced sale is increased (Proposition 5b).  However, the resulting profits are greater than that when the price sensitivity is forecasted to be unchanged (Proposition 5c).  These results, together with Proposition 4, suggest that the firm should take advantage of the lower price sensitivity at the time of consumption to increase capacity utilization, even if price levels are lowered and more discounts are given for advanced sale.

 For the case when the price sensitivity is forecasted to be higher at the time of consumption, the results are a reversal of that when the price sensitivity is forecasted to be lower (Appendix).


 This study shows that advance selling of service capacities is optimal.  It is commonly believed that advanced selling stems from demand uncertainty at time of consumption, or is motivated by competitive preemption.  The findings show that even when these factors are absent, advance selling is optimal.

Across all the cases analyzed, profit maximizing prices result in non-zero excess capacity.  This suggests that it is optimal to plan for excess capacity.  In fact, from Propositions 1, 2a, 3b, 4a, and 5c, it can be generalized that having excess capacity is a dominant strategy.  The corollary thus follows.

Corollary 1:          It is a dominant strategy to plan for, or to have, excess capacity, if the firm’s total capacity is higher than the optimal capacity sold.

 The corollary implies that, in the initially planning of capacity, a firm should intentionally plan for excess capacity.  This is to ensure that it does not miss out on the opportunity cost of lost sales, so that profits can be maximized.  Furthermore, given that fixed costs of operations way out weigh the variable cost of operations, service firms should focus on optimal pricing that maximizes profits, rather that that maximizes capacity utilization.

 However, the notion of holding excess capacity appears to go against the grain of conventional wisdom held by the industry.  The common argument being that, selling the excess capacity even at a much lower price earns something at least, which is better than earning nothing if excess capacity is held.  This argument is valid only if service firms can successfully price discriminate across consumers at the time of consumption.  Otherwise, the lower price for excess capacity drags down the profit maximizing higher price.  Thus, to achieve price discrimination of consumers at the time of consumption, a “waiting list” of consumers can be created to fill the excess capacity at the very last moment.

Corollary 2:  Service firms can price discriminate to maximize sales of excess capacity by creating a “waiting list” of consumers.

 Other plausible ways of achieving successful price discrimination at the time of consumption are suggested by Ng et al. (1999). It has been acknowledged that allocating fixed costs is a problem in service operations.  Some firms distribute fixed costs over its entire capacity, to account for unit capacity costs, in performing break-even analysis (Lovelock, 1991).  Doing so may lead to a sub-optimal price being charged, which is not profit maximizing, as there is the danger that the unit price is being treated as substitute of unit variable cost in determining profit maximizing prices.  Furthermore, by allocating fixed costs over the entire service capacity, firms may become reluctant to price low in advance.  Yet, if prices are not low in advance, a higher price at time of consumption cannot be supported.  Thus, service firms that operate on high fixed cost and low variable cost should be cautious about the allocation of fixed cost across capacity in their yield management practices.

The results show that capacity allocation for advance sales is dependent onprice sensitivity.  Specifically, when price sensitivity is forecasted to be low (high), the capacity allocated for advanced sale decreases.  Hence, from Propositions 3a and 4a, the corollary follows, which provides a guideline for capacity planning.

Corollary 3:           Service firms should increase (decrease) the capacity allocation for advanced sale if the forecast of the price sensitivity at the time of consumption is high (low).   

 Finally, Propositions 4 and 5 suggest that when price sensitivity at the time of consumption is expected to be low, then service firms should increase their capacity at the time of consumption and (even) lower their prices to capitalize on the low price sensitivity.  The corollary thus follows.

Corollary 4:           Service firms should increase the capacity allocation and also lower their prices at the time of consumption if the forecast of the price sensitivity at that time is low.

 Hence, although this work is theoretical in nature, there are several important decision rules that can be deduced (Corollaries 1 to 4) to guide managers in their decisions about the use of advanced sale.


 There are several directions for future research.  This study formulated this model based on supply-side economics in using the Cournot type demand function.  This is done in order to investigate the impact of price sensitivity on advanced sale.  A future research direction is to structure the model based on a Bertrand type demand function.  Doing so would allow the examination of the impact of demand elasticity on optimal pricing and capacity allocation in advanced sale.  The results from the two perspectives can then be compared to gain greater insights in the practice of advanced sale.   Competition has been excluded from this analysis. Work is in progress on an extended model to include competition, with the aim of investigating how competitive interactions will impact advanced sale.  It is for this reason also that, method wise, a game theory based approach has been adopted, to examine the practice of advanced sale in this essay.  It is hoped that this essay will provide greater stimulus to researchers in service marketing to perform theoretical research based on deductive science, hence further increasing the rigor of research in service marketing.


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Weissenstein, Eric, Foes Slam Door on Marriott Plan. Advertising Age (Nov 4 1991):


Proof of Proposition 1: π = (P0k0 – C) = [(α - βk0)k0 – C] ⇒ FOC (wrt k0): 0 = α - 2βk0 ⇒ k0 = k* = α/2β, if k0 = k* < K.  Hence, P0  = P* = α - β(k*) = α/2, e = (K – k*) = (K - α/2β) > 0, and π = (P0k0 – C)  =  [(α2/4β) – C].  If k0 = k* ≥ K, then k* = K ⇒ P* = α - β(K), e = 0, and π = (P0k0 – C) =  [(α - βK)K – C]. ⇒ Profit maximizing price results in excess capacity e > 0.  QED.

Proof of Proposition 2a: π(without advance) = (P0k0 – C) = [(α - βk0)k0 – C] ⇒ FOC (wrt k0): 0 = α - 2βk0 ⇒ k0 = k*  = α/2β, P0  = P* = α - β(k*) = α/2, and e(without advance) = (K – k*) = (K - α/2β).  Hence, π(without advance) = (P0k0 – C) =  [(α2/4β) – C].  On the other hand, π(with advance) = [(PAkA  + P0k0) – C] ⇒ π (with advance) = (α - β(kA + k0))kA + (α - β(k0))k0 – C ⇒ FOC wrt kA: 0 = α - 2βkA - βk0.  FOC wrt k0: 0 = α - 2βk0 - βkA.  Solve simultaneously the two FOCs: k0 = k* = α/3β and kA = k** = α/3β ⇒ e(with advance) = (K - k* - k**) = (K - α/3β - α/3β) = (K -

2α/3β).  Hence, ewith advance = (K - 2α/3β) < ewithout advance = (K - α/2β).  QED. Proof of Proposition 2b: 

Substituting k* and k** into equations (1) and (2), and rearranging, I can derive the optimal price levels, P0(with advance) = P* = 2α/3, and PA = P**= α/3.  Therefore when advanced sale is not undertaken, P0(without advance) = α/2 < P0(with advance) = 2α/3.  QED.

Proof of Proposition 3a: k* = k** = (α/3β) derived from first order condition of profit π w.r.t. capacity. QED.

Proof of Proposition 3b: 

Since π(with advance) = [(PAkA  + P0k0) – C], PA=α/3, kA=α/3β, P0=2α/3 and k0=α/3β, ⇒ π = (α/3)(α/3β) + (2α/3)(α/3β) – C = (α2/3β) – C.  Hence, π(without advance) = [(α2/4β) – C] < π(with advance) = (α2/3β) – C.  QED.

Proof of Proposition 4a: When β0 = βL  < βA, k* = [α/(4βL - βA)], and k** = [α(2βL - βA)]/[(4βL - βAA]. When β0 = β  = βA, k* = α/(3βA) = k**.  Hence, more capacity is allocated for sale at t0, as [α/(4βL - βA)] > α/(3βA), if (4βL - βA) < (3βA) ⇒ βL < βA, which is true.  Less capacity is allocated for sale at tA, as [α(2βL - βA)]/[(4βL - βAA] < α/(3βA), if 6βL - 3βA) < (4βL - βA) ⇒ βL < βA, which is true.  Hence, k*/k** = [α/(4βL - βA)] x [(4βL - βAA]/ [α(2βL - βA)] = βA/ (2βL - βA) > 1 if βA > (2βL - βA) ⇒ βA > βL.  QED.

Proof of Proposition 4b: When β0 = β  = βA, e = [K - 2α/3βA].  When β0 = βL  < βA, e = [K - α/(4βL - βA) - α(2βL - βA)/(βA(4βL - βA))].  Hence, capacity utilization is greater when the price sensitivity at t0 is low as, [K - 2α/3βA] - [K - α/(4βL - βA) - α(2βL - βA)/(βA(4βL - βA))] > 0 ⇒ βA > βL > (βA/4) (after rearranging the inequality).


Proof of Proposition 5a: When β0 = β  = βA, P0 = 2PA = 2α/3.  When β0 = βL  < βA, P0 = α(3βL - βA)/(4βL - βA), and PA = α(2βL - βA)]/(4βL - βA).  Hence, the price level at t0 is lower when price sensitivity at t0 is low as, α(3βL - βA)/(4βL - βA) < 2α/3, if (9βL - 3βA) < (8βL - 2βA) ⇒ true for βA > βL.  The price level at tA is also lower when price sensitivity at t0 is low as, α(2βL - βA)/(4βL - βA) < α/3, if (6βL - 3βA) < (4βL - βA) ⇒ true for βA > βL. QED.

Proof of Proposition 5b: When β0 = β  = βA, P0 = 2PA = 2α/3, resulting in PA/P0 = ½ (or 50% of P0).  When β0 = βL  < βA, PA/P0 = (2βL - βA)/(3βL - βA) < ½ if 2(2βL - βA) < (3βL - βA) ⇒ (4βL - 2βA) < (3βL - βA) ⇒ βL < βA, which is true.  QED.

Proof of Proposition 5c: When β0 = β  = βA, π = [(α2/3β) – C].  When β0 = βL  < βA, π = [[α2βL/((4βL - βAA)] – C].  Hence, {[[α2βL/((4βL - βAA)] – C] - [(α2/3β) – C]} > 0 ⇒ {(βA - βL)(4βL - βA)} > 0 (after rearranging the inequality), which implies that βA > βL > (βA/4). QED.



This paper aims to provide a deeper conceptual understanding of demandbehavior and the pricing of servicesIt argues why all services are sold in advance and show how the specificities of services result in two types of risks faced by buyers who buy in advance, that of unavailability of service and a low valuation of the service at the time of consumption. Furthermore, advanced buyers run the risk of not being able to consume at the time of consumption and this relinquished capacity may be re-sold by service firms. The paper develops a theoretical model that show that advance prices are always lower than spot prices. Also, providing a refund to advanced buyers may be optimal. In addition, the paper show a counter intuitive result that under certain conditions, the firm’s strategy may be pareto optimal in that a guarantee against capacity unavailability and a refund guarantee against valuation risk may be offered to advance buyers at a lower advance price than if a refund offer is not provided. Finally, our study also showed that the firm could earn higher revenue when the risks are asymmetric. Profits are higher when the market faces high valuation risks than when the market faces unavailability risk. 

Key words: Services, Advanced Selling, Yield Management, Revenue, Refund, Pricing INTRODUCTION

 Services now account for a large percentage of the gross national output of many developed countries. The Organization of Economic Cooperation and Development (OECD), informs us that the service sector[4] comprises some of the world’s largest corporations, and these corporations are major buyers and users of advanced technology, are the most active innovators, are facilitating a major reengineering of a growing number of firms across all sectors of the economy, are a major stimulant to productivity and efficiency (through outsourcing services), and, through ecommerce, are having a catalytic effect by transforming and accelerating changes that are already underway in the economy.

 Yet, in a world where a cinema ticket can be obtained for 20p[5], flight tickets at £1[6], and songs at 50p7, even the most discerning marketer might wonder if the pricing for services have gone mad, or if not, perhaps a little out of control. To top it all, there are now multiple prices for buying any service, depending on where you are, why, when and how you are buying. With new technologies, the capability of firms to offer even more innovative pricing options is set to grow (Xie and Shugan, 2001)

 In throwing light on the nature and issues surrounding pricing for services, the industry itself is not helping. Within the service economy lie a heterogeneous set of activities such as financial services, telecommunication, retail, restaurants, transportation, entertainment, education, public services, not to mention not-for-profit activities. Hence, many who attempt to study it have lamented on its diversity. Adding to the complexity of pricing is the fact that many services are bundled with goods, or may be a facilitating service between two or more services and/or goods e.g. a mobile payment service. Yet, price complexity may be useful to service firms, as the customization (versus commoditization) of a service arising from a more direct contact with consumers may result in the consumer being less able to compare prices and consequently, more market power for the firm. 

 How does one, then, begin to study pricing for services? Is there a common thread across all services? Does the pricing of telecommunications services have anything in common with an airline ticket? What can we learn from pricing one service that we can apply into the pricing of another?

This paper aims to provide a deeper conceptual understanding of demand behavior and the pricing of services, specifically services with high fixed costs, low variable costs and short-term capacity constraints such as hotels, airlines, and restaurants.  It begins with a critique of conventional pricing approaches, leading into a conceptual development of pricing in services and argues for four stylized facts. These stylized facts show how the specificities of services result in two types of risks faced by service buyers; that of unavailability of service and a low valuation of the service at the time of consumption. The risk of unavailability of service would drive buyers to buy in advance (to ensure availability) while the risk of valuation would drive buyers to buy at the time of consumption (to be sure that they want to consume it at that time (i.e. value the service). For example, tourists buying flight tickets may buy in advance so that they are assured of a seat while business customers may prefer to buy at the time of consumption when they are more assured of the date they are required to travel. 

The stylized facts also introduce the probability of advanced buyers not being able to consume the service at consumption time and how the capacity relinquished can Online music clubs e.g. MSN Music club be re-sold. Against this backdrop, a theoretical model of pricing for services is developed. 

In the theoretical model, the results show that advanced prices are always lower than spot (consumption time) prices because of higher potential revenue contributed by advanced purchase as a result of the probability of non-consumption. This paper also shows that providing a refund to advanced buyers may be more profitable. This is because the firm is able to obtain higher revenue from higher prices and increased advanced demand from the refund offer. In addition, the study uncovered that when advanced demand is highly price sensitive the firm’s strategy may be pareto optimal in that a capacity guarantee and refund offer is offered to advance buyers at a lower advance price than if a refund offer is not provided. This is because the expanded demand from the refund offer is high enough to provide higher revenue (both actual and potential) to the firm even when advanced prices are lower. Finally, the study also showed that the firm could earn higher revenue when the risks are asymmetric. In other words if the market consists of buyers who face high valuation risks (i.e. are concerned that they would not value the service at the time of consumption), profit is higher than when the market consists of buyers who face unavailability risk (i.e. concerned about unavailability of capacity). This is because when the market faces a high valuation risk, spot prices become higher, thereby contributing to higher revenue earned from re-sold capacity of non-consuming advanced buyers.

 The rest of the paper is organized as follows. In §1, a background of the study is presented with a critique of conventional pricing approaches. This is followed by §2, the development of a conceptual model of pricing services, putting forward four propositions. Following on, in §3, a theoretical model formulation is presented. The model is then extended to incorporate a refund offer in §4. In §5, asymmetric demand functions are considered.   Discussion of the results follows in §6 and the paper oncludes with some remarks and directions for future research.


 One of the most popular, and yet acknowledged as the most ineffective way to price a product, be it a goods or a service, is the use of ‘cost-plus’ pricing. Essentially, this type of pricing sets a price for the product that is sufficient to recover the full costs i.e. variable and fixed costs, and add a sufficient margin above that cost to provide the firm with some profit. Perpetuated by companies who are accounting or operations centered, this approach seems to be financially sound and logical. 

 However, such an approach would pose problems, especially in high fixed cost services. For services like transportation, airlines or 3G telecommunication services, how would one begin to price such that the fixed costs can be sufficiently covered? In transportation and airlines, fixed costs could include the cost of assets such as a cargo ship, or an airplane, whilst for 3G, the fixed cost could be the cost of acquiring the 3G license. Aside from the obviously high costs of these assets, the service may reap the benefit of the asset for the next 20, 50 years or some uncertain number of years. The cost-based price set for each unit of the service is therefore some amount that is a contribution towards the overall fixed (and sunk) costs. This contribution is not only difficult to determine, it is also inconsistently practiced across firms. Hence, when marginal costs are zero, the ‘cost’ computed in the cost-based approach is some amount contributing towards fixed costs and the price is some percentage above that amount, given the volume to be sold. Such a pricing approach may lead to uncertain outcomes when firms begin to compete on price. For example, if a service is less differentiated from its competitor and a price competition results, how low can a service firm price vis-à-vis the other, when each firm apportion its costs differently, and may be changed at a whim? This could explain the downward spiraling prices experienced by the airline industry in the 1980s, after de-regulation (see Levine, 1987 for an analysis of airline competition then). More recently, this problem has again surfaced when aggressive pricing by European low cost airlines has resulted in losses for some, prompting the Chief Executive of Easyjet to comment that pricing by budget and full service airlines are “unprofitable and unrealistic” [7]

 Furthermore, firms who adopt this approach show a lack of understanding of how pricing theory functions. The simplest criticism (e.g. Nagle, 1995) is that costs per unit cannot be determined without knowing the volume to be produced and the volume to produce is dependent on demand that is in turn determined by the price. By setting a ‘cost-plus’ price, the ‘cost’ is at best an approximation.

 Yet, one may still be tempted to argue in its favor by pointing out that since the future is uncertain, the circularity for pricing can never be squared unless some forecast is made of the uncertain demand. Hence, it is not far fetched if the firm is to forecast the demand characteristics, based on historical data, and price its product based on the ‘cost’ of producing that forecasted volume.

 A whole stream of research on demand forecasting and yield/revenue management in high fixed cost services such as airlines and hotels have emerged following this point. In the majority of these studies, the yield management problem is structured as one in which firms maximize payoffs/yield, given some forecasted demand profile (e.g. Badinelli and Olsen 1990; Belobaba 1989; Bodily and Weatherford 1995; Hersh and Ladany 1978; Pfeifer 1989; Toh 1979). Over time, increasingly complex demand profiles, which require increasingly sophisticated mathematical algorithms to obtain solutions, have been introduced and investigated (e.g. Alstrup et. al. 1986; Hersh and Ladany 1978). Most of these studies deal with how much capacity should be allotted for a given set of prices.

 The problem with this approach is three-fold. First, to use an exogenous demand profile where the profile is divorced from both the capacity allocation and pricing decision of the firm is not only unrealistic, it is also wrong. When a firm changes the capacity allocated to a particular price level, the firm should optimally revise that price and demand, in turn, would be expected to adjust. If price levels and demand profile are exogenous, any optimal solution would be a false optimal.  

 Second, without a theoretical structure to explain why demand quantities are the way they are or why they follow a particular pattern across time, there is no assurance that the past is able to predict the future. Pricing needs to be rooted on primitive consumer behavior. Why consumers behave they way they do is just as important as to how they are behaving. Accordingly, despite tremendous computing power available today, pricing based on forecasted demand face the same old problem in conventional probability theory, where according to Bernstein (1996), “the raw material of the model is the data of the past”. 

 Third, demand profiles are subject to a great many factors, not least the actions and strategies of the competitor. To assume that demand based on historical data can still hold for some future may be assuming too much. Consequently, since revenue management fundamentally brings in the pricing behavior of firms, concepts of consumer behavior (demand behavior) should be incorporated. Thus, revenue management is not merely an operational or optimization issue. 

 Given that accounting and the operations management disciplines may not provide a satisfying approach to pricing in services, do the economists have a better handle on this then? As evidenced by the Bank of England’s quarterly report (1998), clearly not:

 “…some of the new service industries may have special economic properties that do not fit well with the assumptions of conventional economic models. For example, telephony and computer software production have high initial costs but very low marginal costs. As a result, pricing strategies may be more complex, and component services are sometimes embedded in customized packages that can obscure the price actually paid or the services actually bought.”

 What this means is that when marginal costs are negligible, as in the case of high fixed cost services such as telecommunication, hotels, or airlines, the cost function is a straight line i.e. it doesn’t matter how much the demand is, the cost is always negligible since all the costs to produce the service had been sunk. 

 Furthermore, since the service is perished immediately upon production, the optimal pricing strategy for the firm is to sell at the point on the demand curve where marginal revenue is zero, that is, if the maximum capacity of the service has not been reached.  Inasmuch as conventional price theory goes, that is the advice. 

 Clearly, the disciplines of accounting, decision sciences and economics take a very different view of costs in services. Whilst the economists contend that sunk costs are sunk and should not feature in pricing decisions, accounting and decision science disciplines insist that pricing decisions have to take into account the return of the fixed costs. To a limited extent, both are correct. To borrow terminology from economics, ex ante, pricing decisions should not take into account costs that had been sunk. However, ex-post, prices obtained may be used to calculate the returns to asset investment. The confusion arises when ex-post analyses attempts to influence ex-ante pricing decisions.  Yet, despite ex-ante decision on pricing that do not consider sunk costs, this paper argues that the research in service pricing have over-simplified the service firm’s pricing decision. The complex pricing programs available in various service industries today clearly illustrates that more needs to be explored.


 Academic service literature informs us that services are unique in that they are perishable, intangible, inseparable between production and consumption, and heterogeneous in delivery, all at once. Furthermore, other distinctively service traits (although not necessarily unique) include high fixed to variable costs ratio and largely temporal in nature. What the literature isn’t too clear about is how such specificities affect pricing. Mere descriptions of service characteristics would not be useful, therefore, unless translated into some meaningful insights that assist firms in the pricing decision.

 Let us take, as an example of a service, pricing a room in a 300-room hotel on New Year’s Eve. The room could be sold 6 months or probably even a year in advance. The mere fact that it can be sold in advance shows that there must be something about the service that causes willingness in a customer to buy before the day itself, factors that I will discuss later. For now, let us think about the value the customer attaches to the room in advance, and that the firm wishes to capture that value in the asking price of the room. This value would not only differ across different customers but even for just one customer, it would differ according to when he wishes to purchase it. If it is too far in advance, he might not even be willing to buy. 

 Let us label the time from when the room has some positive value all the way till its production/consumption on New Year’s Eve as the service’s valuable life, signifying the time span when the service holds some value to some customer somewhere. Figure 1 illustrates a typical difference between a good and a service. Accordingly, the service can be sold at any time during its valuable life (i.e. selling at (1) in figure 1). However, while it is sold at a different time, the very act of producing a service for a customer requires the source to be present, either as man or machine. This means that the production and consumption of a service is simultaneous, as it is widely established (Rathmell 1974; Regan 1963; Johnson 1970; Bateson 1977). Once the room is produced and consumed on New Year’s Eve, it is perished, and its valuable life ends. Since the service can only be sold during its valuable life, and its valuable life ends at the production of the service, it can then be concluded that services can only be sold before production (and consumption, since both are simultaneously held). This is an important point because it alludes to a key difference between services and goods. Whilst goods can also be sold before production, goods firms retain a choice of whether to sell before or after production. This choice is not available to services.

 It must become clearer now that the issue of pricing in service is therefore the issue of advanced pricing, even though the time in advance may be mere minutes e.g. the purchase of a movie ticket just before the movie (cf. Edgett and Parkinson 1993) (i.e. at (3) in figure 1). 

Stylized Fact 1: The perishability and inseparability of services results in all sale of services to be advanced sale and pricing of services to be advanced pricing

 As figure 1 shows, the inventory of a good is with the seller after production and before delivery and with the buyer after taking delivery until consumption. Since services are intangible, there is no question of inventory in the exchange. Rather, if the service customer buys in advance, he faces several risks, which I would elaborate below.  Since production and consumption is simultaneous, the consumer is unable to buy in advance to store and consume at some later date. The consumer can only buy in advance and consume later. Similarly, the firm can only sell in advance and produce later. This is an important point. Conventional economic wisdom informs us that we buy only when the utility we attach to consuming the product outweighs the price we are supposed to pay for it. However, normative economics and marketing literature often implicitly assumes that buyers receive utility at the time of purchase. Since there is now a separation of time between purchase and consumption, it implies that the consumer’s utility is truly obtained not at the time of purchase, but at the time of consumption (cf. Shugan & Xie, 2000). Why is this significant? When there is a separation of purchase and consumption, there is a probability that a buyer who has purchased may not be able to consume, or as academics would term it – the utility becomes state dependent (cf. Karni, 1983; Fishburn, 1974; Cook & Graham, 1977). Put simply, a buyer who buys a movie ticket an hour before the movie might find that he is unable to watch the movie when the time comes because he has fallen ill. How is this different from goods? After all, when a consumer buys a good, the consumption of his good is also dependent on his state at that time. The difference between a good and a service in this regard is that the goods consumer chooses the time (and the state) that is most suitable for consumption, after he has purchased the good e.g. taking a can of coke out of a fridge to drink on a hot day. This is possible because the good is not immediately perished upon production. The service consumer may not have such a luxury since he needs to buy the service first and then consume later, when the state is uncertain. The application of state dependent utility theory into service research was first proposed by Shugan and Xie (2000), when they investigated spot and advance pricing decisions and the optimality of advanced selling. 

 It is important to note that the utility of the service buyer may not drop to zero. It might be that the state of the world has rendered the consumption of the service less valuable e.g. an open-air concert under the rain. Since the buyer faces uncertainty in ascertaining the value of the service at the time of consumption, the buyer faces a risk, which I term as valuation risk

Stylized Fact 2: Due to the inseparability of productions and consumption, and the separation of purchase and consumption, the (advanced) pricing of services would need to take into account the valuation risk faced by buyers

 Of course, to mitigate valuation risk, buyers would choose the time when it is most conducive for consumption and would typically turn up to buy seconds before consumption. I label this time as spot time. However, as many service firms operate with capacity constraints, buyers may not be able to obtain the service if they all show up at that time. Accordingly, if a buyer waits to buy only at spot time, he faces the uncertainty that the service may not be available, and I term this risk as unavailability risk. To alleviate that risk, he may be willing to purchase further in advance of consumption, as insurance (Png 1989), i.e. at (2) in figure 1. Previous literature in advanced selling has shown that advanced purchase is common in many service industries for this reason (Lee and Ng, 2001; Shugan and Xie, 2000, Xie and Shugan, 2001). Consequently, buyers who wish to be sure of obtaining a service would buy in advance.

Stylized Fact 3: Short-term capacity constraints result in buyers facing uncertainty in the service being available, if they choose to buy at consumption time. The pricing of services would need to take into account the unavailability risk faced by buyers Technically, both the purchase further in advance and the purchase at spot is deemed as advanced purchase as proposition 1 has explained. However, for purpose of clarity, I term purchases close to consumption as spot purchases, and purchases further in advance as advanced purchase, consistent with the terminology used by extant literature on this phenomenon. In reality, as elaborated by Lee and Ng (2001), the point where advanced purchase ends and spot purchase begins is industry specific and is also dependent on ‘rate fences’ erected by the seller. Rate fences are constraints or conditions imposed by service firms to ensure minimal cannibalization of purchase. Consequently, the service industry is a host to a wide range of advanced prices also called “forward prices, pre-paid vouchers, super saver prices, advance ticket prices, early discounted fares, early bird specials, early booking fares, and advance purchase commitments” (Xie and Shugan, 2001).

 Clearly, there is a trade off between the buyer’s unavailability risk and valuation risk. Hence, there would exist a market for selling the service in far in advance for buyers who would like to ensure that the service is available, regardless of whether the seller is willing to sell to this market. Similarly, there would also exist a market for selling at (close to) consumption time, for buyers who would like to ensure that they are able to consume. 

 As illustrated in figure 1, the trade-off between unavailability risk (which drives consumers willingness to buy further in advance) and valuation risk (which drives consumers willingness to buy closer to consumption) means that the distribution of demand across the valuable life of the service becomes important in the firm’s pricing decision. In this respect, the firm faces uncertainty in demand distribution across time – if they sell too much too early at too low a price, they may lose the opportunity to earn higher revenue from transient or last- minute customers but if they sell too little in advance, they may be saddled with unused capacity. To many revenue management consultants, accurate demand forecasting, coupled with dynamic optimization algorithms across time is key to better pricing decisions and higher profitability for service firms. 

However, the pricing problem doesn’t end there. The firm’s decision on price would also have an effect on the buyers. For simplicity, let us assume that there exists only two times in the service’s valuable life to sell – advanced time, denoting selling the service far in advance and spot time, denoting the selling of the service at a time close to consumption. If the advanced price is low, the discount from spot price might outweigh the valuation risk faced by spot buyers and similarly, if the spot price is low, advanced buyers might wait till spot to buy. Consequently, there is some degree of cross-time dependence between advanced and spot demand. Once this principle is extrapolated across multiple selling times in a service’s valuable life, one would probably appreciate the full extent of the firm’s complex pricing decision.

 Finally, a crucial difference between pricing for services and goods is embedded in another effect of inseparability. Even if a buyer is to buy in advance, advanced selling requires the buyer to still present himself (or at least, the item that requires the service) at spot time. In other words, since services are inseparable in consumption and production, each advanced buyer has to ‘show-up’ to consume. Especially when the purchase is conditional upon a particular time of consumption, there would be a fraction of advanced buyers who may not be able to consume the service during the specified consumption time. This is commonly acknowledged in various revenue management literature, where attempts have been made to structure various reservation policies to minimize the impact of the cancellation and ‘no-show’ concept of advanced selling. (e.g. Alstrup et al. 1986; and Belobaba 1989; Hersh and Ladany 1978; Lieberman and Yechiali 1978; Rothstein 1971, 1974, 1985; Thompson 1961; Toh 1985). What has not been discussed is that the existence of a non-zero probability of non-consumption by advanced buyers provides a service firm with a unique opportunity not presented to goods firm i.e. the ability to sell the capacity that was already sold in advance – again at spot. This re-selling capability may then translate into additional profit for the firm either in the additional spot sales or overselling beyond the firm’s capacity in advance. If this sounds distinctively implausible, let me illustrate this point through two examples.

First, tow truck services operate with limited capacity but sell (albeit at a very low price) through the AA (automobile association) an enormously large number of its services in advance. Since the fraction of the market that actually require a tow truck service may be low, the firm obviously oversells its capacity in advance as well as re-sells them at spot (at a high price for those who did not buy in advance). Second, IT support services are usually oversold to buyers in advance, since the fraction of non-consumption may be high.

 The implications on the pricing decision are enormous. Depending on the demand distribution across time, the level of non-consumption and capacity, firms might be prepared to manipulate advanced and spot prices to optimize profits. 

Stylized Fact 4: Separation of purchase and consumption due to the inseparability of services result in a non-zero probability of advanced buyers not consuming, thus freeing up of capacity to be re-sold at spot. Pricing decisions have to take into account the non-consumption effect.

 In the following section of this paper, I examine this advanced sale phenomenon, given the above propositions, through the development of a theoretical model. Few literature have investigated this phenomenon. Shugan and Xie (2000) showed that due to the state dependency of service utility, buyers are uncertain in advance and become certain at spot while sellers remain uncertain of buyer states at spot because of information asymmetry. They suggest that advance selling overcomes the informational disadvantage of sellers and is therefore a strategy to increase profit. Xie and Shugan (2001) studied when advance selling improve profits and how advanced prices should be set. They have also investigated the optimality of advanced selling, investigating selling in a variety of situations, buyer risk aversion, second period arrivals, limited capacity, yield management and other advanced selling issues. 

 Png (1989) showed that costless reservations in advance is a profitable pricing strategy as it induces truth revelation on the type of valuation that consumer has for the service (which is private information). If the consumer has a high valuation i.e. able to consume, he would exercise the reservation and pay a higher price. If not, the consumer would not exercise. In another paper, Png (1991) compared the strategies of charging a lower price for advanced sale and attaching a price premium at the date of consumption versus charging advanced buyers a premium and promising a refund to advanced buyers should consumption prices be lower than what was purchased. 

 However, despite various literature modeling the phenomenon, there has been no attempt to uncover the theoretical foundations that drive the primitive consumer behavior of advanced selling i.e. the notion of why advanced and spot demand exists, or how demand dynamics function within this domain. Although the above literature model sellers’ pricing strategies on advance selling, the fundamental aspect of pricing lies in demand behavior. This demand behavior, particularly in the advanced selling context, should not be exogenous and needs to be understood in at least two dimensions. First, as modeled by Xie-Shugan, Shugan-Xie and Png, it is important to understand the way buyers react to changes in prices by their choice of buy, not-buy or switch to buy at a different time. Second, it is also important to know how many would decide on each of the choices. In the latter, the heterogeneity of buyers is a necessary factor and has yet to be studied.

In studying this phenomenon therefore, three primary differences are highlighted between this study and those above. First, I do not model the individual consumer as one (or more) set of homogeneous consumers. Instead, I model the consumers as heterogeneous, through the use of demand functions. By modeling the consumers’ price sensitivity, I capture both the decision of buyers to buy or not to buy as well as the quantities of each choice at a given price. Second, I also model the substitutability between advanced and spot demands, capturing the buyers switching decision, as well as the quantities that switch for a change in price. Finally, the model explicitly captures the probability of advanced buyers not being able to consume at the time of consumption and I analyze how this impacts on pricing. Through this model, it is hoped there will be greater applicability in the characterization of the phenomenon. 

 This study extends the previous paper in chapter 2 of this dissertation (i.e. Lee and Ng, 2001). Chapter 2 proposed an advanced selling model and studied the optimality of advanced selling and the optimal prices and capacities for advanced sale and at the time of consumption. In that model, it was implicitly assumed that all advanced buyers are able to consume at the time of consumption, and that only such buyers are captured within the demand functions. Furthermore, the model assumed that demand at spot is independent of advanced price. In this study, I relax both the assumptions by explicitly modeling advanced buyers probability of non-consumption, and by incorporating the interdependency between the demands at spot and advanced time.  All proofs of propositions are found in the appendix.


I now proceed to specify the model. The following is defined:


Price per unit of the service sold at advanced time where 



Price per unit of the same service sold at spot where 



Profits to the service firm



Quantity of service demanded by the market at advanced time



Quantity of service demanded by the market at spot



Capacity of the service firm and K > 0



Advanced time

t0          =         Spot time

 A service sold in advance and at the time of consumption is not unlike two firms selling products differentiated only by the time of sale. The difference is that since there is only one service firm, the profit maximized is derived from demand at both times. Consequently, we can adapt product differentiation models derived in economics literature.  Following  Dixit (1979) and Singh and Vives (1984), we assume the following demand structure for selling the service at tA and t0 :

q0 =α−βP0 PA qA =α−βPA P0 where β> 0, δ> 0 and β>δ

 Forms of this demand curve have been used in marketing modeling literature e.g. Mcguire and Staelin (1985), who modeled the decision of two manufacturers and their choice to intermediate when the demand faced by both are represented by linear demand functions similar to that modeled above, and Ingene and Parry (1995) who modeled two competing retailers also facing similar demand functions, and how a manufacturer would coordinate the channels.

Capacity and State effect

 The parameter δ depicts the effect of increasing PA on q0 and increasing P0 on qA . The assumption β>δ means that the effect of increasing P0 ( P A ) on q0 (q A ) is larger than the effect of the same increase in PA (P0 ). This is a reasonable assumption because the price of a service may be more sensitive to a change in the quantity at its own time than to a change in the quantity across time, in other words, to borrow the terminology used in models of this nature, own-time effect dominates cross-time effect. This could be due to several reasons. Since the services are differentiated by time of sale, the time difference may create other uncertainties to the buyers and thereby result in a lower cross time effect.

 Note that the parameter δ, in the context of advanced selling of services, can be deemed to capture the valuation and unavailability risks faced by buyers. This means that a change in spot price would have an impact on advanced demand and the degree of impact is dependent on the magnitude of δ. It is assumed, for convenience, that the demand functions are symmetric across time. This assumption will be relaxed later. Thus, if the unavailability and valuation risks are low, δmay increase implying that there is increased substitutability between buying in advance and at spot.

 I parameterize the probability that a buyer who buys in advance, but is unable to consume as ρ where 0 < ρ<1. Note that the portion of demand sold in advance who are unable to consume at t0 can be equivalently depicted as ρqA . This capacity could be re-sold to buyers at spot, and at the spot price of P0 , yielding a revenue of P0ρqA to the firm (the assumption of full ability of the firm to re-sell relinquished capacity will be relaxed further on in the study). Finally, the firm may be constrained by its overall capacity i.e. q0 + qA K

 Given the situation described above, the objective function of the service firm becomes: MaxpA,p0 [πq0 + qA K]  where π= PAqA + P0q0 + PqA

 In summary, I include into the model the specificities of services in the following manner:

  1. The existence of advanced demand qA , due to the credibility of capacity constraints, as well as the existence of demand at spot due to valuation risk
  2. The substitutability parameter, δ, which I interpret as cross-time substitutability, and which captures the trade off between demand reaction to the two types of risks in the market.
  3. The ability of services to re-sell capacity sold at tA and not consumed at t0

The following are the model assumptions. 

  1. While I model the proportion of non-consuming buyers, I assume that this proportion, together with the consumer demand parameters, is common

knowledge to the firm and the market i.e. there is full information.

  1. The marginal costs of providing the service is negligible as service firms in general operate with high fixed costs. This is consistent with research in this area (e.g. Kimes 1989; Desiraju and Shugan 1999).
  2. The capacity has no salvage value after production/consumption.
  3. The service under study is a pure service (with no goods attributes). This means that the consumer, after consumption, has no ownership of anything tangible. This is as opposed to a good/service mix where the consumer, after consumption, may also own a good (e.g. a seminar with course materials). The re-selling issue may not apply to the ‘good’ part of the product since the consumer may not return it after buying in advance.
  4. Prices at spot and in advance are positive i.e. PA,P0 > 0.
  5. The firm can credibly commit to spot prices in advance (cf. Xie and Shugan, 2001)
  6. Buyers who buy in advance are guaranteed the availability of capacity at time of consumption.
  7. Capacity relinquished by advanced buyers can be fully re-sold at spot (this assumption is relaxed in the next section)
  8. The service is not transferable.
  9. The firm is a monopoly.

 The Xie-Shugan model depicts the phenomenon as a two-period process where homogeneous consumers arriving in period 1 can decide to buy or wait after the firm announces their spot and advance prices. Consumers may also arrive in period 2. In reality, buyers are not merely heterogeneous in their valuation of the service (i.e. own time price sensitivity). They are also heterogeneous in their willingness to switch between spot and advanced time (cross time sensitivity).  

 In Png’s model, the advance buyer, should he chooses to buy, knows how much he values the service only at the time of consumption. The probability of the buyer turning out to be a low or high valuation customer is depicted as λ (in XieShugan, it’s q). This model incorporates this feature with ρ. If the buyer is able to consume, he is deemed to be a high valuation buyer. If he is unable to consume, he is deemed to have a low valuation. However, a key difference is that Png assumes a low valuation customer obtains a low valuation regardless if he is able to consume i.e. if he consumes, he receives a low valuation and if he does not, he will enjoy a low valuation net of any price paid for the alternative (p.250). Although it may not make a difference to the customer who obtains a low valuation regardless, his willingness to consume the service has a direct effect on the firm. If he does not consume, the capacity can be relinquished and re-sold. This ability to re-sell obviously impacts on the price of the service, both in advance and at spot. In all of Png, Xie-Shugan and Shugan-Xie’s models, this ability had not been considered and I incorporate it here.


 When capacity is higher than optimal demand (interior solution), the constraint is non-binding and I provide the following lemma as a benchmark:

Lemma 1: When ρ= 0 and the capacity constraint is non-binding, PA* = P0* =  

which we denote as P* (0), and q*A = q0* = α2 which we denote as q* (0) and π* = βα−2 δ) which we denote as π* (0). 2(

 If the fraction of non-consumption capacity is zero ρ= 0 , i.e. all advance buyers are able to consume, prices and quantities sold at t0 and tA would be the same, due to the symmetric demand functions. As I have argued previously, ρ will always take on a positive value. Consequently, prices and quantities at t0 and tA start diverging, as the following proposition show:

Proposition 1:         When ρ> 0 , and β>δ , the firm derives a higher profit by lowering

advanced price and increasing spot price, obtaining higher advanced demand and

lower spot demand. However when  , the firm

derives a higher profit by increasing both advanced price and spot price, obtaining higher advanced demand and higher spot demand. The firm’s optimal prices and quantities are:

   PA* = P* (0)(1− S[2(β−δ) + ρ(β− 2δ)])    P0* =P*(0)(1+S[2(β−δ)+βρ])    q0* =q*(0)(1−S[2(β+δ)+ρ(β+2δ])    q*A =q*(0)(1+S[2(β+δ)+βρ])


                         S =   

 As every unit of advanced demand provides an opportunity to the firm to resell, the firm chooses to lower PA* to obtain higher advanced demand when price

sensitivity is high enough i.e. β>δ . Due to cross time sensitivity, a lower PA*

decreases spot demand. However, instead of compensating by lowering P0* to obtain higher spot demand, the firm chooses to increase P0* instead since resold capacity can be sold at a premium and the marginal revenue from re-selling capacity at a premium (through higher P0* ) is higher than marginal revenue derived from increasing spot demand through a lower P0* . A graphical representation of the above can be seen in figure 2. However, when own-time price sensitivity is very low relative to cross-time price sensitivity, i.e.  , both advanced and spot prices

increase with the probability of non-consumption because the cross-time effect of an increase in spot price causes a high advanced demand, sufficient to negate a reduction in advanced price. The net effect is that both advanced and spot prices increase. Notice that a positive advanced price is conditional on . This means that the ability of a firm to obtain positive revenue from selling in advance is dependant on the degree of own-time price sensitivity vis-à-vis the cross-time price sensitivity. Furthermore, S exists only when ρ> 0. Thus, while the terms proceeding S determine the level of increase or decrease in prices and quantities, we can intuitively label S as the non-consumption effect attributable to the advanced purchase of services.

<Take in Figure 2>

 To highlight the impact on profit, the difference in expected profits (after some manipulation) can be written as Lemma 2:  π* −π* (0) = (1) + (2) + (3) + (4) − (5) where

  • ρP* (0)q* (0)
  • ρP* (0)q* (0)S [2(β−δ) +βρ]
  • ρP* (0)q* (0)S [2(β+δ) +βρ]   
  • ρP* (0)q* (0)S 2 [2(β+δ) +βρ][2(β−δ) +βρ]
  • 2P* (0)q* (0)S 2 ⋅[4(β2 −δ2 ) + 4β2ρ+β2ρ2 − 4δ2ρ]       

 The first term is the added profit due to double selling the fraction of nonconsuming capacity, ρq(0). The second shows that the capacity that is double-sold is sold with a price premium of S ⋅[2(β−δ) + βρ]. The third term shows that the advanced demand also increases, amplified by the combination of both own-time and cross-time sensitivities i.e. S ⋅[2(β+δ) + βρ]. This amplification is because advanced demand is made higher through both a higher spot price and a lower advanced price. The fourth term shows that the increase in advanced demand also enjoys the same price premium. Finally, the fifth term captures the loss in revenue as a result of a lower advanced price and a lower spot demand. 

 Lemma 1 is consistent with Xie-Shugan’s model where it was shown that when marginal cost is low and capacity constraint is non-binding, advance and spot prices are the same. However, unlike Xie-Shugan, the findings show that advanced prices may be lower even when marginal costs are zero as the presence of ρ creates the divergence in advance and spot prices. Clearly, the potential revenue from one unit of advanced sale is higher than that from spot sale. Therefore, advance price decreases to generate a higher advanced demand. The cross time effect of this is an even higher spot price.

Asρ increases, firm has greater incentive to price PA lower to stimulate advanced demand. This amplifies the decrease in spot demand, pushing P0 even higher.

Proposition 2:      When          own-time          price          sensitivity          is          high          i.e.

                              β>δ⋅2(ρ+  2 ⋅  2 + 6ρ+ 5ρ2 + ρ3 ) , the greater the probability of

                                                         4 + 4ρ+ ρ2                           

non-consumption, the higher (lower) the quantity sold in advance (at spot) and the  lower (higher) the advance (spot) price i.e. , ∂∂qρ0* < 0.

 In Png’s model, a strategy of selling firm advance order does not maximize profit because the advanced buyer is unwilling to pay a higher price due to unavailability and valuation risk. Yet, a firm advanced order usually guarantees availability and as XieShugan model showed, firm advance orders can be optimal.  In addition, Png’s model does not take into account the fact that a buyer who buys in advance has a non-zero probability of not consuming and that non-consumption frees up the capacity to be resold. By modeling in non-consumption, I show that the potential revenue from advanced sales increases and it may be optimal for the firm to sell in advance.

 Png’s model also showed that the seller’s revenue from spot sales is zero because buyers would prefer the non-contingent alternative in advance rather than wait till spot where the seller would extract all the consumer’s surplus (i.e. high price). Where the market is heterogeneous in the form of a demand function, the optimal price at spot assumes not all surpluses are extracted from everyone. Consequently, there is also heterogeneity in the degree to which customers may be willing to wait till spot or buy in advance. This implies that both spot and advanced demand would exist, with some degree of substitutability between buying at these two times, as modeled here.

Accordingly, there is an optimal price at both times, as set out above in proposition 1.


 Providing refunds for buyer’s inability to consume is widely practiced in the airline industry. Casual enquiries by the author with airlines sales offices indicated that many airline tickets are sold with some refund value. Some tickets even provide a full 100% refund to the customer. Generally, a full refund means that the ticket purchased can be returned to the airline for a full reimbursement of the price at any time – even after the proposed date of travel. This means that if the buyer cannot make a flight for any reason, the airline is fully prepared to return the price of the air ticket to the customer without any penalty fee, no questions asked. Furthermore, many airlines allow a refund on non-utilized sectors, e.g. if you have purchased a return ticket but only utilized one leg of the ticket. 

 There is a fundamental difference between a full refund of this nature and those given out by retail shops for goods purchased or by service firms after the consumption of the service. In the latter, the refund is given (or promised) if the firm fails the consumer i.e. the compensation is provided to the buyer due to firm’s failure to deliver the benefits, according to the buyer’s perception. In the former, and also the focal point of this study, refunds are promised for buyer failure i.e. when the buyer fails to consume the service, through no fault of the firm. Here, the buyer does not even have the basis to complain about the service, since he has not consumed it. Yet, current airline practices show that the firm still provides him with the refund. 

 Png’s (1989) model attempted to shed some light into this behavior. While he found that firm advance orders are not optimal, his study showed the profit maximizing strategy is to insure the risk averse customer by compensating him when his valuation low and charging him high when his valuation is high. Thus the optimal result was a costless reservation at advanced time for all advanced buyers and a higher price for the high valuation customer at spot with the low valuation buyer not exercising the reservation. In this way, the advanced buyer is fully insured against capacity unavailability, and partially insured against his valuation at spot time. However, this strategy requires the advanced buyer to face the risk of capacity being unavailable at the time of consumption. In other words, Png’s advanced buyer does not actually buy the service; he merely buys the option of buying the service at the time of consumption, at a stipulated price i.e. a price option. This is in contrast to the buyer failure refund of the type illustrated above where it is clear that the buyer buys with a firm advance order (where capacity is guaranteed) with a refund in the event of a low valuation. 

 Consequently, I extend the model to investigate the circumstances where the firm may have to provide a refund, r, as a fraction of the advanced price. The firm could be compelled to provide r due to reasons such as competition. The question I attempt to answer is whether the firm could benefit from that provision. I assume that there is a speculative market at tA such that it prohibits the firm from providing any refund above a full refund i.e. 0 < r 1. In addition, the condition of the firm’s ability to re-sell the service at spot is relaxed and I introduce µ, the probability of the firm being able to re-sell relinquished capacity at spot. For this extension, no assumption is made on the impact of r on demand. While it might be possible that a refund offer may increase advanced demand, I do not assume that here. Hence, the demand functions could be seen as demand faced by the firm already with the refund offer into the market. 



PA,R offer 

Price per unit of the service sold at advanced time, with a full refund



Price per unit of the same service sold at spot



Profit to the service firm when a refund is offered to advanced buyers



Quantity of service demanded by the market at advanced time



Quantity of service demanded by the market at spot



Refund (if offered) by the firm where 0<r 1





Probability of capacity relinquished by advance buyers being re-sold at


I model the firm’s decision through an extensive form game (shown in figure 3),

with perfect information but uncertain, with nature deciding if the firm is or isn’t able to re-sell the relinquished capacity sold in advance.


 First, let’s consider the impact of µ, if the firm does not need to offer a refund i.e. r = 0 . From proposition 2, an increase in probability of non-consumption ρ decreases advanced prices and increase spot prices overall. However, for a given ρ, if µ is low, the impact of ρ is lessened even if ρ is high. The presence ofµ therefore reverses the effect of ρ. When the firm has a low probability of re-selling at spot market, advanced prices increase because the inability to sell reduces the firm’s incentive to price lower and stimulate the advanced market. Thus, prices in advance increase and spot prices decrease. To the extent that if µ→ 0, PA*,R ,P0*,R P* (0) . 

 From the game tree in Figure 3, r=0 is a first best solution. However, when is it more profitable for the firm if a refund has to be offered (i.e. second-best)? The firms’ profit would be higher with a refund offer when: Max{Erefund ]} > Max{Enorefund ]}

The proposition below illustrates the benefit of offering a refund:

Proposition 3:        The firm obtains higher profit when a refund is offered if and only if the cross-time

price sensitivity is low i.e. when δ< β⋅φ where


                             and 0<φ<1 for all permitted values of 0<ρ<1 and 0<µ<1

and the firm prices at 

PA*,R = P* (0)(1− SR[2(β−δ) + ρ(βµ+δr) − 2µδρ)]), P0*,R = P* (0)(1+ SR[2(β−δ) + ρ(βµ+δr) − 2βrρ]) and obtains  q*A,R = q*(0)(1+ SR[2(β+δ) +ρ(βµ−δr)]), q0*,R = q* (0)(1− SR[2(β+δ) +ρ(βµ−δr) −ρ(2βr −2δµ)])

where SR =   

                          Note that SR S when {r → 0,µ→1}.

 Xie-Shugan showed that firm advanced orders with a refund offer may be optimal as the firm is able to obtain a higher price in advance to compensate for the cost of refund, as well as derive greater profits from cost savings in not having to serve these customers.  The above proposition shows that the firm may derive higher revenue from higher prices and/or expanded advanced demand with a refund offer.

 If the cross-time sensitivity is low i.e. δ< β⋅φ, the refund offer increases profit. This is because when the cross time impact of a price change is low (i.e. less customer switching), a change in price at (advanced) spot time would have less an impact on (spot) advanced demand. Low cross time sensitivity implies state sensitive people are more concerned about a conducive state and won’t switch to advanced time even if the price is low. Similarly, capacity sensitive people are more concerned about obtaining the service, and won’t switch to spot time even if the spot price is low. Consequently, a refund offer effectively reduces the price paid for the advanced buyer who might not consume without any impact on the buyer who would consume. When cross time sensitivity is low, this reduction in price does not cause as many spot buyers to switch, hence the firm obtains higher profit from higher revenue in advance without losing much spot revenue.

Proposition 4:          Where the values of µ and ρ are such that µρ(1+µρ)−2(1−ρ) > 0, the

firm offers the boundary solution of a full refund (i.e. r* =1) when

δ≤ µρ(12+−µρρ(1)−−µρ2(1)−ρ) ⋅β.


 Since the firm is constrained by speculators, it cannot increase refund beyond giving a full refund. Thus, the above proposition spells out the level of cross-time sensitivity, below which the refund offered is a boundary solution of a full refund. As noted previously, the ability to re-sell capacity relinquished by advanced buyer contributes to the level of advanced price. Consequently, under the condition of full ability to re-sell, I find the following counter intuitive proposition:

Proposition 5:          When µ=1, β>  and refund is offered (under the condition in proposition3)

and  r < 2β3ρ(2 + ρ) + 4δδ32ρ(1[+2δρ+) −ββ(22δ−(ρ4 +−ρ3ρ2 )]2 + ρ3 ) − 4βδ2ρ2 ,   PA*,R < PA* .

 In contrast to Png and Xie-Shugan models, the above proposition show that advanced price may be lower with a refund offer. This is because the firm derives higher revenue from two sources. First, the revenue from increased demand may outweigh the revenue from increased price. Second, with an expanded advanced demand, the potential revenue from non-consumption also increases. Consequently, when price sensitivity is high relative to cross-time sensitivity i.e. β> , the result

could be pareto optimal where it is possible for the firm to provide the advanced buyer with insurance against capacity unavailability (through a positive advanced purchase price), and insurance against valuation risk (through a refund offer), at a price that may be lower than if a refund was not offered. 

Asymmetric Cross-time Sensitivity

 As noted earlier, the parameter δ, in the context of advanced selling of services, can be deemed to capture the trade off between valuation and unavailability risks. Clearly, they do not need to be the same i.e.

q0 =α−βP0 +δ0 PA qA =α−βPA AP0 where β> 0 and β>δA ,δ0

 To investigate the implications of asymmetry, I take two extreme examples i.e. δA 0 and δ0 0. For simplicity, I assume no refund need to be provided and capacity relinquished by advanced buyers would be fully sellable i.e. µ=1, r = 0.

When δA 0

 In this case, advanced demand is not affected by spot prices. However, spot demand may be affected by prices in advance and at spot. This type of behavior can be typical of a market segment where capacity availability is extremely important. Such services may include those where the time of consumption is tied to the value of the service e.g. an important flight, a hotel room on New Year’s eve, anniversary dinner etc. In these cases, the advanced market cannot be persuaded to wait to buy at spot. If the price in advance is too high, they would seek alternatives at advanced time (and therefore drop out of the market) instead of switching to spot. I term this as a market that faces high unavailability risk.

When δ0 0

 In this case, spot demand is not affected by advance prices. However, advanced demand may be affected by prices in advance and at spot. This is type of behavior can be typical of a market segment where a conducive state for consumption (i.e. high valuation) is extremely important. Such services may include emergency services e.g. tow truck, or computer/equipment support services where the precise time the service is required is often not known. In these cases, the spot market cannot be persuaded to buy in advance (high valuation risk). If the price at spot is too high, they would seek alternatives at spot time (and therefore drop out of the market for the service). I term this as a market that faces high valuation risk.

 In practice, a service firm can face a market with both high valuation and unavailability risk. For example, a flight from London to New York would be a service with high availability risk for a passenger that needs to get on a particular flight e.g. to attend his son’s graduation. He would therefore purchase in advance. If the price is too high, he would choose an alternative (perhaps another airline) but he would not consider waiting till spot, as he needs to be in New York at that particular date. In contrast, a business executive who does not know when he might be called to go on a business trip to New York would not be swayed to buy in advance, as he is unsure if he should be traveling on that date.

Proposition 6:           A market that faces high unavailability (valuation) risk pays higher prices in

advance (at spot) than a market that faces high valuation (unavailability) risk if β> .

 While the above proposition is not surprising it is found that the firm’s profit from asymmetric states is not the same. Since high unavailability risk can increase advanced price, it also serves to reduce advanced demand as a result of the increase in price. This would reduce the potential revenue from non-consumption.


 This study aims to offer a more applicable model of advanced selling as a phenomenon. In stylized fact 1, I justify why all services are sold in advance and that the pricing of services is in fact advanced pricing. In stylized facts 2 and 3, I proceed to explain how advanced demand may be distributed between a time further in advance until consumption due to the specificities of services, specifically through the heterogeneous levels of unavailability and valuation risks faced by the service consumer. In stylized fact 4, I illustrate one particular idiosyncratic factor of advanced selling in services i.e. the fact that advanced buyers may not be able to consume and thereby releasing the capacity to be re-sold by the firm at the time of consumption. Having conceptualize the factors which influence pricing in services, I then proceeded to model the phenomenon through a theoretical model incorporating consumer price sensitivity, degree of cross-time substitutability as well as the ability of the service firm to re-sell relinquished capacity at spot time to derive the optimal pricing and quantities. 

 The model presented found, in proposition 1 that advanced prices are lower than spot prices even without binding capacity constraints or marginal costs because the potential revenue from one unit of advanced sale is higher than that from spot sale. Proposition 2 showed that as the probability of non-consumption increases, advanced price decreases and spot prices increases further. This result may be applied to emergency or support services where buyers can buy in advance with very low probability of consumption. As noted earlier, an example of this would be breakdown services where buyers buy in advance but their consumption date is uncertain. What may be viewed as insurance could in fact be a form of advanced purchase at a very low price. Without advanced purchase, the spot price of breakdown services would be high, as many who own older vehicles would attest. Another example of advanced purchase at a low price at a low probability of consumption would be the purchase of support services e.g. equipment or IT support. Such services are often sold to individuals or firms on an annual subscription basis. Again, without a support contract, the spot prices of such services are usually much higher.

 The higher potential revenue from advanced demand diminishes, however, as the ability of the firm to sell relinquished capacity reduces. Services that required capacity scheduling or planning at the time of consumption such as removal or exhibition services may fall into this category, where spot and advanced prices may be the same. Yet, this proposition may accentuate the realization that such firms could explore creative pricing strategies or use technological advancements to improve overall profit through the ability to manipulate the parameters set out in this model. 

 For buyers who buy in advance but are unable to consume (i.e. buyer failure) I find, in proposition 3, that offering a refund could be more profitable when cross time sensitivity is low relative to own-time price sensitivity. This is because when cross time sensitivity is low, the firm benefits either from increase price and/ or increased advanced demand. Proposition 4 showed that a full refund may also be optimal under conditions of very high own-time price sensitivity relative to cross-time sensitivity. However, proposition 4 is a boundary solution. Consequently, if the firm is able to increase cross-time sensitivity, profit may be increased with an interior solution for r. A counter intuitive result was discovered in proposition 5, where a refund offered to advanced buyers could result in a lower advanced price than if refund was not offered. This occurs when the refund offer is able to expand demand to such a degree that it is optimal for the firm to lower its advanced price.

 Asymmetric conditions where unavailability and valuation risks are not equal are then investigated. Not surprisingly, I find in proposition 6 that advanced prices are higher when unavailability risks are high and spot prices are higher when valuation risks are high. That means that the perception of high valuation and high unavailability risks may be profitable for the firm. However, if the risks are too high, buyers may not buy. In practice, such risks may be manipulated by the firm through its pricing policies, for example, by providing some degree of flexibility in the time of consumption. Where the business executive facing high valuation risk in not being certain which date he is to fly, a flexible flight time might persuade him to buy in advance or lower his valuation risk, increasing his willingness to pay. Similarly, if the graduate’s father is given a ticket that allows him to choose a range of flight times thereby reducing his unavailability risk, he might be swayed to buy at the last minute or lower his perception of risk, which would in turn increase his willingness to pay. A summary of the results of the model can be seen in Table 1.

<Take in Table 1>

 This model also resolves the issue of risk aversion in buyers through the use of a demand function. Since buyers in advance are heterogeneous in their valuation of the service, their probabilities of non-consumption are also heterogeneous. As advanced buyers would also discount this uncertainty in their valuation, this ‘discount’ alludes to the buyers’ degree of risk aversion. The demand function, and its parameters, subsumes such complexities. The result is a parsimonious model that offers a theoretical framework on how service pricing could be approached. 

Revenue Management

The objective of revenue management studies is to maximize yield by managing the sale of service capacity over time, through pricing, capacity allocation, and timing of sale (Badinelli and Olsen 1990; Desiraju and Shugan 1999). One of the limitations of current revenue management (RM) literature is that many RM models maximize payoffs/yield, given some forecasted demand profile that is largely exogenous, and does not explicitly capture the price/capacity relationship (e.g. Badinelli and Olsen 1990;

Belobaba 1989; Bodily and Weatherford 1995; Hersh and Ladany 1978; Pfeifer 1989; Toh 1979). A widely used approach in this stream of literature is that of mathematical programming. Kimes (1989), however, commented “although the linear programming solution can be found, the assumption of deterministic demand makes the solution to the problem unrealistic”. The model developed here is based on demand functions across time, hence capturing fundamental concepts of consumer behavior (cf. Chase 1999; Lieberman 1993; Relihan 1989). 

 The model can be applied in a non-linear programming approach, where the firm can obtain the optimal price and quantity sold at each (continuous) point in time as:

Pt* = Arg maxPt πn0 q(t)dt K    for     t ∈{0,1,2...n}    and qt* =α(t) −β(t)Pt* +δ(t)P0*

Where  qt =α(t) −β(t)Pt +δ(t)P0 q0 =α(0) −β(0)P0 + ∫n0δ0 (t)Pt dt and π= ∫n0 Pt qt (Pt ,t)dt + P0 ∫n0 ρt qt (Pt ,t)dt 

 Essentially, the firm faces a demand function at any point in advance that incorporates some degree of substitutability between the price at that time and the t0 price. Each demand parameter is time dependent and the distribution of that parameter across time may be defined exogenously. The optimal price and quantity to be sold is generated from the objective function that multiples the quantity sold at each time with the price at that time, cumulative across time. Each advanced time would also yield a fraction that may not consume at t0 and that is accounted for in the objective function.

Hence, the price and quantity relationship is captured explicitly as is the substitutability between times of purchase. Price discrimination, by a simple extension, can simply be incorporated into the model as the standard “affine” pricing schedule where the firm realizes a price premium at any point in time equivalent to the social surplus at the optimum at any time t. Similarly, multi-leg flights, 3 nights hotel stays etc. can be modeled in as a bundled product and its demand function ascertained accordingly. By using the demand function, a vast quantity of economic literature can be applied into the advanced selling context to produce richer insights into the strategy of advanced selling.

 In the past, where pricing is often static, the above specification might have been difficult to implement. However, the advent of the connected economy, where data can be obtained quickly, technological innovations have made complicated algorithms possible to implement. Thus, a dynamic pricing model such as the one suggested above is not impossible. Furthermore, B2B and B2C marketplaces create new ways to exchange goods and services, and the firm has to innovate for higher revenues. What is left is the question of how many prices should the firm introduce to the market before it becomes confused (see Desiraju and Shugan, 1999).


 The purpose of this chapter is not merely an additional model of revenue management or to capture optimal pricing strategies. What I seek to show, by abstracting the phenomenon into this theoretical framework, is that the parameters modeled here provides the firm with various strategic levers to influence demand and plan an effective pricing strategy. As noted earlier, degree of flexibility may affect the perception of risk. Depending on the type of service industry, further research and managerial creativity may find other levers that would manipulate the demand faced by the firm and provide the firm with unique opportunities for higher revenue.  It is fairly common knowledge that many low cost airlines price according to the amount of capacity available, increasing prices as the plane fills up, and according to demand forecast. Consequently, revenue management is capacity and forecast centered rather than value centered. Through the model, I aim to provide a more satisfying approach towards revenue management, as a practice. My contention is that the firm’s focus should not merely be on revenue management as it should be on revenue improvement. Thus, only when demand behavior is incorporated into the equation, providing a more complete picture, the firm would be able to understand where and how its revenue is being obtained. As our model has demonstrated, the offer of refund could increase profit for the firm. However, firms may misunderstand that refund is merely a form of providing ‘quality’ service and as such, might be tempted to drop the offer, as some low cost airlines do. In so doing, they miss the opportunity to derive higher profit either through expanded advanced demand or higher advanced price.  For several decades, developed economies have been service economies. The expansion of the service sector is partially attributed to an increase in the intangible component (often known as the service component) of the production of agricultural and industrial goods. Conversely, the past decade has also shown that certain service activities are embracing some degree of industrialization. The resultant convergence has prompted some scholars to propose that the service economy is moving into “an economy based on service relationship as a mode of coordination between economic agents” (Gallouj, 2002). As such an economy expands, faster than even academic analyses can hope to catch up with, fundamental questions gets left behind, to the extent that they may become less fashionable to conduct research on, although the issues may be no less important. Such fundamental questions include, though not limited to, some basic differences between goods and services. As goods and services are becoming increasingly similar in nature (Gallouj, 2002), it is more important that their differences are addressed. Only when the difference between a good and a service is properly specified, can there be a greater understanding of the broader context in which a combination of a good and a service function. Further research could also explore pricing issues in the bundling of a good and a service, the costs of advanced selling, especially transaction costs, the impact of a binding capacity constraint (cf. Xie and Shugan, 2001) and the impact of competition on advanced selling strategies.

 By abstracting the phenomenon of advanced pricing in services through a stylized model, I aim to provide a platform for a more thorough understanding of pricing in services, across various service industries. 


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