Creating the Experience Economy in E-Commerce - Chang

contributed articles

122 communications of the acm | July 2010 | vol. 53 | no. 7

doi: 10.1145/1785414.1785449

by Wei-Lun chang, soe-tsyr yuan, and caroL W. hsu

AdvAnces in informAtion technol o gy together with the forces of globalization have accelerated the growth of service industries. In 2003, the OECD reported that service industries now account for over 60% of both employment and the gross domestic product (GDP) of OECD member countries. The U.S Bureau of Labor Statistics (BLS) has forecast strong employment growth in the American service sector between 2004 and 2014. Although service industries are expanding, Gilmore and Pine argue that, the growing commoditisation of services offered has gradually transformed the competition for market share from focusing on the quality of services to the creation of memorable experiences.4 As a consequence, the competitive position of a firm now depends to a large extent on its ability to generate impressive experiences through innovative delivery channels.

In this article, we adopt Gilmore and Pine’s view that the economic value of the experience economy lies in co-producing the staging experiences via customer participation and connection.4 Furthermore, we suggest that current technologies and the growth of the Internet have both enabled and strengthened

the opportunities for experience-ori-ented offerings beyond limitations of time and place. In following sections, we first describe the current practice of experience economy in electronic commerce. Taking the iCare health care service as an example, we demon-strate how collaborative pricing over the Internet can further provide added-value to the production of experiences offered in the electronic marketplace.

experience economy in e-commerceIn discussing the evolution of econo-mies, Pine and Gilmore9 observed that the economic value of a given offering increases as its form changes from a commodity, product or service to an ex-perience. In Pine and Gilmore’s view, a product has the highest economic value when it becomes an experience product,9 for example, when consump-tion of the product focuses on the pro-cess of staging a “memorable” experi-ence. Compared with other economic offerings, the creation of experience consumption depends on the extent of a customer’s participation and absorp-tion during the consumption process. Therefore, while the key attribute of service product involves focusing on the extent of customization, the domi-nant element of experience offerings lies on the level of personalization. The more personalization a business manages to create, the more the cus-tomer will be prepared to spend. Pine and Gilmore offered a number of ex-amples from the entertainment and retail industries. The leading example is Disneyland, where customers pay to immerse themselves in distinctive and memorable experiences.

In recent years, the Internet and intensified market competition have motivated organizations to pay more attention to the significance of expe-rience offerings. The Internet infra-structure has reduced the cost of coor-dination, and thereby accelerated the commoditization of services.7 At the same time, competitive pressures con-tinue to press the need for product dif-

creating the experience economy in e-commerce

July 2010 | vol. 53 | no. 7 | communications of the acm 123


process of engagement, the customer is given a memorable experience. Nike, the world largest athletic shoemaker, embraced the potential of e-collabora-tive design in 1999 by offering custom-designed men’s and women’s shoes over the Internet.

In addition to physical goods, the development of digitized products ap-pears to a great opportunity for imple-menting experience-centered business models.13 For example, digital content design services empower producers and consumers so that they can ef-fectively co-create digital content in a novel collaborative way without the constraints of time and place. In the entertainment sector, the growing popularity of online interactive gam-ing also offers experiences unknown to previous economic models. The play-ers come together to enjoy the thrill of interaction in cyberspace. We suggest that the success of YouTube lies in its ability to create a platform that not only allows people to contribute con-tent, but also to immerse themselves in experiences through video created by others in the global community.

These examples demonstrate how customers can actively participate in the consumption process in the virtual world. In this article, we propose the concept of collaborative pricing as an extension of collaborative design to create an experience economy on the Internet. We believe that offering col-laborative pricing could create better entertainment and education experi-ences, and thereby increase the eco-nomic value of such offerings in the electronic marketplace.

experiencing collaborative Pricing in e-commerceSimilar to the concept of collaborative design, collaborative pricing allows cus-tomers to become active participants in deciding the prices (for example, the amount they are willing to pay) for ser-

ferentiation and innovation in order to gain a competitive edge in today’s fast-moving business environment. There-fore, the business value and potential profits generated by experience offer-ings are becoming increasingly attrac-tive and vital to firms that endeavor to remain competitive in the global mar-ketplace. As Pine and Gilmore9 note:

“Increased price volatility as mar-ket forces take over awaits the sellers of all commoditized goods and services. Companies that stage experiences, on the other hand, increase the price of their offering much faster than the rate of inflation simply because consumer value experiences more highly.”9

In this article, we argue that the power and capability of the Internet al-lows the creation of experience-orient-ed consumption beyond these limita-tions, as shown by the implementation of collaborative design and pricing ap-plications on the Internet.

collaborative design in the e-commerce environmentToffler and Toffler11 coined the term “prosumer,” a combination of produc-er and consumer, to describe people who consume the products they cre-ate. In electronic commerce, online collaborative design is an example of how the prosumer concept can be used to provide experience offerings. For in-stance, Koren6 revamped collaborative filtering recommendation approaches by modeling time changing behavior, which can track dynamic customer preferences for products. By creating a process that involves consumers in the design and development of a product, a company can ensure that the product meets the consumers’ needs. It would also engender a sense of ownership among customers that leads to intense customer loyalty.10 Many such business strategies have been implemented. For example, an e-interior design service enables designers and customers to co-operate in the interior design of a house.1 Customers can alter their re-quirements, and the e-interior design service encompasses certain cognitive components (for example, design con-cepts) to adapt a user’s design demands automatically. As a result, through the

vices tailored to meet their changing needs. We define collaborative pricing as “a model that allows customers and providers to participate and to jointly decide prices with the underlying ob-jectives of maximizing the willingness-to-pay and optimizing profits.”

In the conventional pricing model of electronic commerce, pricing strategies are based primarily on mass customiza-tion; allow limited customer participa-tion in price determination (See Table 1). We suggest the current form of in-formation technology also has the po-tential for creating a collaborative pric-ing process, which would add to the economic value of digital goods.

The proposed collaborative pricing process involves four steps, namely, set-ting price differentials, anchoring and collaboration, e-service molding, and price optimization (See Figure 1(a)). In the first step, producers set differ-ent prices for various versions of an e-service according mass customization. The consumer then chooses a specific version, which provides the anchor for future collaboration about pricing. The process then elicits consumers’ feed-back and modifies the services accord-ingly. This molding phase continues un-til a consensus is reached, after which an optimal price will be generated.

icare: an example of collaborative PricingWe believe that implementing a collab-orative pricing model can contribute substantially to experience-generation in the digital entertainment and health care industries. For example, eCare has utilized Web technology to im-prove the quality of health care via ex-periences. However, existing aging ser-vices (health care or eCare) are mostly oriented towards clinical gerontology (such as, exercise technology, sensor technology) or the neuropsychology of aging (such as, pre-symptomatic diag-nosis of age-related cognitive decline,

table 1. conventional and collaborative Pricing Process

conventional Pricing Process

Steps understanding the budget

Setting price differentials

Considering psychological dimensions

Establishing prices

Making price revision decisions

collaborative Pricing Process

Steps Setting price differentials

Collaborating to determine actual needs

Co-shaping the product/service

Establishing prices

Making price revision decisions



devices for the amelioration of age-related changes in human sensory and motor systems). The present design of eCare lacks quality dimensions, such as community involvement, consumer participation and continuous quality improvement, which are crucially im-portant to increasing the value of care for an aging population.3

We have implemented a platform called iCare (intelligent Care) (see Fig-ure 2),1 which extends eCare technolo-

gies to a domain of ambient e-services that addresses the concept of experi-ence offering. The objective is to pro-vide quality e-services to elderly people anywhere-anytime via an iCare home portal. The features of iCare e-services include ambient service accessibility, attentive personalized service provi-sion, innovative life-style creation, pre-cious digital memory, and seamless social connections. The concept of iCare is unique through the provision

of attentive personalized service. Such warm-hearted features would not only provide elderly people and their fami-lies with essential services, but also allow them to engage in a virtual col-lective decision-making process about the services they would like to have (via a case-based model, brainstorming model, or garbage can model).

During the process of interaction, the model collaboratively generates bundled services for the elderly to expe-rience and prices the bundled services based on the maximum willingness-to-pay (WTP) and optimal profits. The fi-nal price results from the collaborative process, the charges for prototypes and the testing effort involved.

collaborative Pricing ProcessHere we define a collaborative pricing model (CPM) as a model that has the objectives of optimizing WTP (CUtility) and profits (PProfit), that allows consum-ers and producers to actively participate and collaborate when pricing products or bundles, and that generates an opti-mal bundle price at equilibrium.

CPM = Optimal(PProfit, CUtility)

In iCare, the formulation of prices that the elderly and their family mem-bers would be willing to pay is based on the collaborative design and pricing method (see Figure 1(b)). The consum-er first chooses the version of bundled services that would satisfy his/her cur-rent needs. The interaction with the consumer and the generation of proto-types continues until those needs are met. After this phase has been complet-ed, the collaborative price (CP) will be estimated based on the prototype test-ing efforts, the design fee for customi-zation, and the cost of the services.

The three principal components of collaborative price (CP) estimation are the design fee, the number of bundles, and test effort involved. The design fee (D) is estimated based on the optimal utility (U); the number of bundles (B) is associated with the test effort (T); and the bundle cost (C) is the cost of the set of ser-vices in the bundle. The following formu-la incorporates the three components:

CP ≥ D(U) + T(B) + C.

Using some experimental evalua-tion, our proposal model (CPM) can be compared against another pricing

(b) The flow of the collaborative pricing process

figure 1. understanding collaborative pricing

(a) The collaborative pricing process



estimates prices compared with CPM for bundles of services when the prob-ability is 0.5 (Table 2(b)). The reason is that EUT only considers the final utility and bundle probability, and ignores the efforts of the collaborative process. If a customer is satisfied with the inter-action process, the utility will be high, and the price can increase. That is, prices may be under-estimated when

approach, the expected utility theory (EUT). The optimal willingness-to-pay and profits attempted by CPM can then be evidenced by some experimental evaluation results as benchmarked against EUT (see Table 2). In the experi-ments, we assume that the number of services in a bundle ranges from 2 to 8 and the outcome probability of choos-ing a specific service bundle is set to

0.9 to 0.5, which may exceed the aver-age weighting. It is sufficiently high to verify the difference between EUT and CPM, except when the outcome prob-ability is 100%.

When the probability is 0.9, EUT will probably over-estimate the price for 2 services in a bundle and under-estimate the price for 5–8 services in a bundle (Table 2(a)). EUT always under-

figure 2. the platform architecture of icare

table 2. evaluation results of cPm benchmarked against eut

Probability=0.9

No. of Service 2 3 4 5 6 7 8

utility (CPM) 16.7184 22.032 27.6048 28.7712 32.4 46.008 54.432

utility (EuT) 23.22 30.6 38.34 39.96 45 63.9 75.6

Average Price (CPM) 39.13515 59.13508 80.60968 90.73046 114.5507 180.8644 221.7905

Average Price (EuT) 49.02 64.6 80.94 84.36 95 134.9 159.6

Average Profit (CPM) 13.33515 25.13508 38.00968 46.33046 64.55069 109.8644 137.7905

Average Profit (EuT) 23.22 30.6 38.34 39.96 45 63.9 75.6

(a) The case when the outcome probability is 0.9

Probability=0.5

No. of Service 2 3 4 5 6 7 8

utility (CPM) 5.688 11.664 16.2 18.72 21.024 21.384 27.36

utility (EuT) 7.9 16.2 22.5 26 29.2 29.7 38

Average Price (CPM) 24.32725 53.43773 85.83707 102.5955 137.6745 148.1236 206.3607

Average Price (EuT) 23.7 48.6 67.5 78 87.6 89.1 114

Average Profit (CPM) 8.527252 21.03773 40.83707 50.59552 79.27447 88.72362 130.3607

Average Profit (EuT) 7.9 16.2 22.5 26 29.2 29.7 38

(b) The case when the outcome probability is 0.5



the number of services in a bundle is large (such as, 4–8 services when the probability is 0.9) (Figure 3(a)). Prices are always under-estimated when the probability is very low (such as, 0.5), as consumers are not satisfied with, and will not accept, the offered services (Figure 3(c)). The experiment results indicate that EUT generates the highest profits when there are 2 or 3 services in a bundle and the probability is 0.9, and generates the lowest profits when there are 5–8 services in a bundle (see Figure 3(b)). However, EUT only considers the final utility and the probability that a bundle will be chosen, and ignores the effects of the collaborative process. Bundle prices are always under-esti-mated when the probability is very low (e.g., 0.5) as the provider under-esti-mates profits (see Figure 3(d)).

In short, the experimental results of utilities for EUT and CPM demonstrate that EUT may over-estimate the util-ity from the consumer’s perspective, whereas CPM can potentially achieve equilibrium (for example, neither over-estimate nor under-estimate the util-ity). The experiment results demon-strate that EUT either over-estimates or under-estimates prices, resulting in higher or lower prices than those obtained with CPM. The results also indicate that collaborative pricing is profitable from the producer’s per-spective. In summary, the experiment demonstrates a positive outcome for implementing collaborative pricing in the electronic commerce context.

summaryThe potential economic value of ex-perience-oriented offerings has been demonstrated in the physical market-place. In this article, we suggest the widespread use of the Internet and ex-isting technologies allow us to extend the experience economy to the virtual marketplace. The growing practice of online collaborative design demon-strates the potential for providing the experience economy via the Internet. Our major contribution is that we pro-pose expanding existing practices by incorporating the concept of collabor-ative pricing into the design of experi-ence offerings. Moreover, we propose a collaborative pricing model based on a platform called iCare, which can be implemented in the Internet envi-

(a) Simulated prices for CPM and EUT (Probability=0.9)

(b) Simulated profits for CPM and EUT (Probability=0.9)

(c) Simulated prices for CPM and EUT (Probability=0.5)

(d) Simulated profits for CPM and EUT (Probability=0.5)

figure 3. evaluation results of cPm benchmarked against eut



ronment. We hope that this article will motivate further research into the de-velopment of the experience economy in electronic commerce.

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Wei-Lun Chang ([email protected]) is an assistant professor at BA Department, Tamkang University.

Soe-Tsyr Yuan ([email protected]) is a professor and Director of Service Science Research Center, National Cheng-Chi University.

Carol W. Hsu ([email protected]) is an associate professor in the Department of Information Management at National Taiwan University.

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Creating the Experience Economy in E-Commerce - Chang

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