matching intermediaries for information goods in the presence of direct search: an examination of...

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Decision Support System

Matching intermediaries for information goods in the presence

of direct search: an examination of switching costs and

obsolescence of information

Manish Agrawala,*, Govind Hariharanb, Rajiv Kishorec, H.R. Raoc

aDepartment of Information Systems and Decision Sciences, College of Business Administration, University of South Florida 4202,

E. Fowler Avenue, CIS 1040, Tampa, FL 33620, United StatesbKennesaw State University, Kennesaw, GA 30144, United States

cUniversity at Buffalo, Amherst, NY 14260, United States

Received 6 June 2003; received in revised form 7 May 2004; accepted 8 May 2004

Available online 17 June 2004

Abstract

This paper investigates patterns of revenues earned by an intermediary that matches buyers and sellers in the presence of

direct search markets. We develop a theoretical structure and a computer simulation model of such a marketplace where vendors

are horizontally differentiated, and an intermediary matches clients to the optimal vendor for a fee. The model is applicable to

information services such as application service providers (ASPs). The contribution of this paper is the identification of

scenarios under which intermediaries that match clients and vendors are likely to be profitable considering switching costs and

obsolescence of information.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Electronic intermediary; Information economics; Simulation; Search

1. Introduction

Intermediaries are entities (computational, human

or organizational) that can be utilized as a bridge on

an information stream between separate (perhaps

distinct or even incompatible) entities to tailor,

0167-9236/$ - see front matter D 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.dss.2004.05.002

* Corresponding author.

E-mail address: [email protected] (M. Agrawal).

customize, personalize, or otherwise extend functions

as the information flows along the stream [3].

Electronic intermediaries and exchanges are a rela-

tively recent business service model aimed at leverag-

ing the benefits of the Internet by bringing together

large numbers of buyers and sellers and facilitating

transactions between them [1,12]. Their promise has

led to the creation of many intermediaries in the recent

past, though few have survived. The failure of such a

large number of public exchanges in spite of the

s 41 (2005) 20–36

M. Agrawal et al. / Decision Support Systems 41 (2005) 20–36 21

promise of the business model suggests that research

is needed to identify scenarios where intermediaries

may profitably add value for buyers and sellers [33].

This research is a step in that direction. We examine

intermediation scenarios where the goods and services

traded may be classified as information goods to

identify cases where intermediaries may be profitable.

A distinguishing feature of information services is

that the marginal cost of production, differentiation

and transportation and of serving additional customers

is very low and often close to 0 [30]. These properties

considerably simplify our analysis and help us focus

exclusively on the matching properties of intermedia-

tion, while still considering a very realistic scenario

[23]. Examples of information service providers are as

diverse as Application Service Providers (ASPs),

travel systems like SABRE and financial services. In

each of these cases, there are many providers offering

essentially similar services [10,34]. Though the

existence of numerous providers is generally benefi-

cial to users and indicates a competitive market, their

existence implies that in choosing a service provider,

clients have to evaluate a large number of providers,

making search an important and potentially expensive

issue [13,15,22].

The aim of this paper is to investigate potential

revenue patterns for intermediaries that provide

matching services for buyers and sellers of informa-

tion services. We consider an information services

marketplace with one intermediary that has perfect

knowledge about the characteristics of vendors and

clients and provides matching services. The interme-

diary exists alongside a direct search market. All

clients have the same willingness to pay for services

while vendors are horizontally differentiated. This

implies that each client may have its own preference

for the ideal vendor. Vendors participate in both the

mediated and direct markets but each client max-

imizes its utility from trade by choosing either the

intermediary or direct search. The intermediary earns

revenues through the fees it receives from its clients.

We use a computer simulation test bed to introduce

clients, vendors, and the intermediary into the

marketplace and compute the resulting intermediary

revenues. We also examine the consequences of

consumption of services by clients during search on

intermediary revenues. To model the fact that clients

may start with some prior knowledge of the market,

we examine the impact of client experience from

repeated search on intermediary revenues. For each

case, we consider different sizes of the vendor

population and examine the intermediary’s revenue

maximizing fee.

The contributions of the paper are as follows. In

the context of information services, the paper identi-

fies the impact of direct search markets and changing

fees on the profitability of matching intermediaries.

We also consider the impact of clients’ knowledge of

the marketplace on the intermediary’s profits. Finally,

we look at the influence of the industry in which the

intermediary operates, specifically the rate at which

the information gathered by clients in earlier searches

loses fidelity in representing the distribution of

vendors in the marketplace.

The paper is organized as follows. Section 2

reviews the research on intermediaries and search.

Section 3 describes our basic research model and the

research context of information services. In Section 4,

we describe the simulations and consider changes in

the pricing policy of the intermediary under different

conditions of direct search. Specifically, we consider

random search, search with and without consumption

and the impact of search experience. Finally, Section 5

concludes the paper with a discussion of the results

and ideas to extend this research.

2. Prior research

This research develops a model to compare the

utility received by clients from matching services

provided by an intermediary when the alternative is

direct search. This section summarizes results from

prior research on intermediaries and search models

that are used in building our model.

2.1. Intermediaries

Intermediaries can play many roles in a market-

place. A familiar scenario is a market maker that

increases the likelihood of completing transactions

[24]. Rubinstein and Wolinsky [27] examined inter-

mediaries as market makers when both buyers and

sellers had the option of transacting directly with each

other or through the intermediary. The intermediary

bought products from sellers and sold them to buyers,

M. Agrawal et al. / Decision Support Systems 41 (2005) 20–3622

choosing a buy price and sell price that maximized its

profits. Its competitive advantage was based on the

greater likelihood of a customer meeting an interme-

diary than a seller. In this formulation, the interme-

diary’s profits were explained by the time-consuming

nature of direct trade, which allowed the intermediary

to extract some surplus for providing immediacy in

matching demand.

The distinction between market makers and match

makers was first clarified by Yavas [37]. Market

makers correspond to the traditional definition of

intermediaries such as retailers who set buy and sell

prices to trade on their own account. This creates the

bid–ask spread in financial markets and inventory

markup in retailing [8]. Their primary service is

immediacy [9,37]. This service becomes particularly

important when there is some uncertainty in levels of

demand and supply and market makers use their

pricing policies to maintain inventories [8,31]. Match

makers on the other hand do not buy or sell but

simply match two parties. Examples of market makers

include used-car dealers and examples of match

makers include real-estate brokers. It has been shown

that market making is more profitable than match

making when the valuations of buyers and sellers are

common knowledge. However, when the valuations

are private information, match making is more

profitable and yields higher welfare when search is

relatively inefficient and costly. It has also been

shown that the presence of an intermediary reduces

the equilibrium search intensities of both sellers and

buyers [37].

Bakos [2] examined the implications of a reduction

in search costs by the use of electronic marketplaces

and suggested that commodity markets could move

closer to Walrasian markets which assume that buyers

are costlessly and fully informed about sellers.

Assuming that buyers start with the knowledge of

distribution of seller prices, he found that by reducing

search costs, electronic markets could prevent market

failures in differentiated markets. Also, by emphasiz-

ing product information over price information using

such intermediaries, sellers could maintain profits.

The role of an intermediary considered in this

research is that of a match maker that facilitates

search. This is appropriate because buyers have

private values for a number of information services.

To decide which market to participate in, consumers

compare the utility of an intermediary’s match making

services with the expected utility from direct search.

In the initial model, clients start with no prior

knowledge of the distribution of vendor offerings. In

later models, we extend our model to examine the

influence of client’s prior information.

2.2. Search

The theory of consumer search was introduced by

Stigler [32] to explain the observed dispersion of

prices in markets. Kohn and Shavell [14] summarized

some of the early results regarding search rules.

Search is generally seen as sequential sampling from a

population where the samples could be prices, product

features, etc. Consumers engaged in search are

generally assumed to have a probability distribution

for samples over the population, which could be

known with certainty or could be uncertain and

subject to revision in light of new information [36].

In general, the expected utility maximizing decision

rule takes the form of a switch point utility level s.

Search ends if the best available utility exceeds s and

continues otherwise. When the distribution is uncer-

tain, s could change as new samples are drawn. When

current costs become relatively more important than

future benefits, for example when risk aversion or

discount rate increases, the switch point level falls,

resulting in reduced search. The switch point also falls

as search costs increase, again reducing the level of

search [14].

When clients do not start with prior information

about the distribution of vendors in the marketplace,

the switch point in the search rule can in general only

be characterized in terms of a functional equation that

is impossible to solve explicitly [14]. Rothschild [26]

showed that dynamic programming can be used to

describe the optimal search rule in terms of the

searcher’s current beliefs even from unknown distri-

butions. Essentially, search stops when the sampled

value is superior to the best-expected utility at the

time of sampling after considering search costs. This

rule also generally satisfies the switch point feature

described by Kohn and Shavell [14]. Rothschild’s

search rule is the basis of the direct search model in

this paper.

Though search rules in the literature are generally

based on sequential sampling [20], another possible


search mechanism is based on obtaining all samples at

once. This is called the fixed-sample-size (FSS)

search strategy. FSS search and sequential search

may be considered special cases of a general search

strategy in which the searcher obtains more than one

sample at a time and then has to decide how many

more times to sample [17]. FSS and sequential

searches each have their advantages and disadvan-

tages. FSS search allows information to be gathered

quickly, though it may lead to overinvestment in

information-gathering. Sequential search avoids

unnecessary information-gathering costs but is slow

in gathering the information [21]. It is therefore

suggested that optimal search should combine the

speed of information-gathering provided by FSS

search with the flexibility provided by sequential

search to avoid unnecessary costs. The important

result for our research is that optimal search reduces to

sequential search under certain conditions. Specifi-

cally, optimal search reduces to sequential search if

the search problem has no decision horizon; the

searcher enjoys full recall and has no rate of time

preference [21]. In the real world, the decision horizon

cannot be indefinite. However, as long as the search is

based on careful evaluation, we consider the decision

horizon to be long enough to make sequential search

appropriate to our analysis. We have therefore used

sequential search in our model.

Sequential search has also been used extensively in

other related applications in the IS literature on

gathering information in order to make decisions

[19]. For example, distributed problem solving has

been modeled as sequential information-gathering and

communication, followed by the final decision [7].

The task of designing inductive expert systems to

classify objects based on their description has also

been viewed as a problem of developing an optimal

sequential information acquisition strategy [18].

3. Basic model

Commercial transactions involve a search by

clients for services and vendors, followed by nego-

tiations and final delivery of services over the contract

period (for example, in bThe Sourcing Life-CycleQ, 6/26/2000, The Gartner group calls it the sourcing life

cycle in the context of Information Services) [11].

Since the matching role of intermediaries is related to

search, we focus our attention on search and develop

progressively complete models of direct search in an

information services marketplace and examine inter-

mediary revenues for the different cases considered.

We utilize the result from prior research that under full

recall, no time preference and long decision horizons,

optimal search reduces to sequential search.

As already described, this paper examines the role

of matching intermediaries in the specific context of

information services. Information services have min-

imal marginal costs of serving additional customers

[30]: they also have low to negligible marginal costs

of production, differentiation and transportation. We

use these properties to simplify our model and focus

on the matching properties of intermediation.

Matching is trivial if clients and vendors are

identical. However, goods and services in any market-

place are generally differentiated along characteristics

such as features, price, availability, information,

support, etc. This differentiation could be either

horizontal or vertical [35]. Horizontal differentiation

applies to cases where the optimal product choice at

equal prices depends upon the particular consumer.

There are no good or bad services. Instead, whereas

consumer A may prefer service X over service Y,

consumer B may prefer service Y over service X.

Vertical differentiation applies to cases where all

consumers agree over the most preferred set of

characteristics and over the preference ordering

among services. An example of vertical differentiation

is quality. Though a consumer may choose to buy a

service of inferior quality based on budget constraints,

most consumers agree that higher quality is preferable

to lower quality.

We consider horizontal differentiation to be the

appropriate choice for information services, as clients

may prefer vendors on many criteria including geo-

graphical location, references and service features. We

therefore examine a market where a population of

clients is interested in identifying an optimal provider

from a horizontally differentiated population of

vendors.

There are two models commonly used to study

horizontal differentiation [35]. In the linear model,

vendors are assumed to be located at different points

on a straight line. In the circular model, vendors are

located around a circular city. The straight line model


is convenient to use while considering a market with

two vendors. However, when considering a large

number of vendors in a market, the circular model is

more tractable. Since our model can have a potentially

large number of vendors, we used the circular model

using Salop’s circle where the product space is the

circumference of a unit-circle [28].

The circular model of product differentiation was

introduced to study the location of services by

vendors. Consumers are assumed to be located

uniformly on a circle with perimeter 1. Consumers

buy one unit of a product or service and face constant

transport costs in using the service. Following the

general horizontal differentiation model, every vendor

is assumed to be located at exactly one point on the

circle, which defines its product specifications, and to

charge the same price for its services. Vendors are

selected based on the bmatchQ between their service

specifications and the optimal specifications of each

client. Transport costs may also be considered as the

costs associated with the mismatch between desired

and received service specifications for clients. In Fig.

1, we show six vendors located around the circle.

Since we consider a horizontally differentiated mar-

ketplace, all clients are characterized by a type u,

which is the price they are willing to pay for the

service, and an ideal service specification li*, where i

is the label for clients. The horizontally differentiated

vendors charge PV for each unit of their service and

are characterized by the specifications lj of their

service, where j is the label for vendors. The

distribution of clients and vendors in this product

space makes the services imperfect substitutes. This

eliminates price competition where each client selects

vendors based exclusively on price.

A population of clients seeking services, vendors

providing these services and one intermediary enter

Fig. 1. Salop’s circle.

the market at a certain time, labeled as 0 in Fig. 2.

Each client buys one unit of a service from a vendor

that maximizes its expected utility net of costs. The

client chooses either direct search or mediated search

to maximize its expected utility. In mediated search,

the client is guaranteed the best possible match for a

fee. In direct search, the client avoids the fee but

incurs search costs. Clients enter the market with no

prior knowledge of the distribution of vendor service

characteristics. In direct search, clients have to

develop their beliefs about the market through

sequential sampling.

The information available to clients in our model is

the following: a rule to evaluate the utility of each

vendor, awareness of the existence of the intermediary

which provides matching services and of a forum

where vendors advertise their services that enables

clients to draw their samples for evaluation. At the

start of a search, the intermediary informs each client

of the utility it can attain by contracting with the

vendor identified by it and the intermediary’s fee Pint

to disclose the identity of the vendor. As observed in

prior research, utility functions are useful when

comparisons involve uncertain values as in this paper

where clients compare their expectations from direct

search and mediated search. Modeling the market-

place as being horizontally differentiated over a circle

implies that clients are also aware of the minimum

expected utility from a service.

Providers who list with the intermediary generally

pay a listing fee. However, it was shown by Bhargava

and Chaudhary [4] that an intermediary would prefer

to include as many vendors as possible and it could

achieve this by reducing vendor participation fees. We

consider the limiting case where the intermediary

charges no listing fees to participating vendors, as a

result of which all vendors in the market choose to

participate in the mediated marketplace. This is in line

with what we observe in many online marketplaces

where the value of the intermediary’s service increases

with the number of participating vendors. The only

revenues to the intermediary in our model are

participation fees from clients that choose to use it

to identify the best available vendor for the client.

When clients use the services of the intermediary, in

return for the intermediary’s fees, they can complete

their search in one single time period and obtain

services from the best matching provider in subse-


quent time periods. Clients that choose direct search

use sequential search to identify a suitable vendor and

use a termination rule to stop search when future

searches are not expected to improve utility [21]. If

the players enter the market at t=0 and we consider a

total of T time periods for search and consumption,

Fig. 2 shows that mediated search terminates at t=1,

whereas direct search can last for tD time periods.

In our model, we assume that only complete

information may be gathered from the intermediary.

A client may either pay the intermediary a fee Pint to

identify the optimal vendor or pay nothing to the

intermediary and choose to search for a vendor on its

own. This assumption was described by Salop and

Stiglitz [29] as follows. There exists a newspaper that

publishes full information and a client can either pay a

fee Pint to purchase and process all this information or

choose not to purchase the newspaper. This is the

basic tradeoff in our model. Clients choose to use the

services of an intermediary when an intermediary

provides utility net of fees which exceeds that from

direct search.

Most search models assume that clients know the

prior distribution of prices or service specifications in

the marketplace, though they do not know who offers

what. In our model, we start by assuming that clients

have no knowledge of the distribution of service

offerings in the product space. This would be

particularly true when they are new services or

services which are constantly evolving. While per-

forming direct search, clients draw inferences about

the parameters of the marketplace through the

sampling process. The advantage of this approach is

that it helps us identify the changes in intermediary

revenues as clients develop greater knowledge about

the marketplace.

Starting with no prior knowledge of the market is

useful in at least two ways in the context of

intermediaries. One, it creates the most favorable

Fig. 2. Intermediated a

conditions for an intermediary because the profit-

ability of the intermediary in our model is dependent

on the utility of the information it provides to clients.

Also, in many markets, particularly those for infor-

mation services where technologies and business

models are rapidly changing, clients are not likely to

start with much knowledge of the distribution of

offerings in the market.

Net utility for client i frommediated search has three

components: (1) expected utility from consumption

UiI(li*,lint,i) based on clients’ preferences in the product

space where lint,i represents the specifications of the

vendor identified by the intermediary and UiI denotes

client i’s utility from using the intermediary; (2) vendor

fee PVand (3) intermediary fees Pint. Utility from direct

search has two components: (1) expected utility from

consumption UiD(li*,ldir,i) where ldir,i represents the

specifications of the vendor identified in search, UiD

the utility obtained by client i by using direct search,

and (2) vendor fee PV. As described earlier, lint,i and

ldir,i may be different because lint,i is the best available

vendor identified by the intermediary but direct search

may stop after identifying ldir,i before the optimal

vendor is identified. The clients’ selection rule states

that it should select mediated search when mediation

yields greater expected utility than direct search and

vice versa. If we consider a total of T time periods, the

search rule may be written as:

Select Intermediary when

XT

UiI li4; lint;i� �

� PV

#"

� Pint NXT

UiD li4; ldir;i� �

� PV

#"ð1Þ

Choose direct search otherwise.

Here the LHS represents utility from mediation and

the RHS represents utility from direct search. Vendors

nd direct search.

1 We thank an anonymous reviewer for this observation.


whose service specifications differ from the ideal

specification have a lower utility for a client, as

specified by the clients’ preferences in product space.

These preferences are modeled using a constant

mismatch costs model so that U(li*,lj)=u�c|li*�lj|

where the distance |li*�lj| refers to the shortest arc

around the unit circle between li* and lj [28]. c is the

cost of mismatch of specifications. Given the decision

rule of clients (Eq. (1)), the intermediary attempts to

set the fee Pint that maximizes its profits. Following

the model of horizontal differentiation, we set the

clients’ willingness to pay, u to a constant value for all

models which keeps utilities from all vendors in the

circular product-space to be non-negative.

Using the constant mismatch model, over the

duration of the arrangement, which lasts for T time

periods, client i’s utility UiI in Eq. (1) is given by:

UiI ¼XTt¼1

u� cjli4� lint;ij � PV

� �#� Pint

"ð2Þ

3.1. Direct transactions without the intermediary

When clients perform direct search, they examine

vendors in the market sequentially until their search-

termination rule indicates that further search is

unlikely to improve utility. The search algorithm used

in the research is based on Rothschild [26]. The

client’s utility from direct search may be split into two

components: one during search and the other after

search is completed. Adding the two, client i’s net

utility is given by:

UiD ¼XtD�1

t¼1

u� cjli4� ltj j � PV � sc� �

þXTt¼tD

u� cjli4� lDj j � PV

� �ð3Þ

where ljt are service specifications of the vendor being

sampled at time t, ljD is the vendor identified from

direct search, sc is the evaluation cost per time period.

As described earlier, direct search may not identify the

optimal vendor when the termination rule suggests

that search be stopped. The cost of direct search

therefore has three components: a fixed cost sc to

evaluate a vendor, opportunity costs associated with

loss of utility during search and loss of utility from

identifying a sub-optimal provider at the end of the

search. Increasing search cost sc decreases the client’s

utility from direct search and expensive evaluation

costs could make intermediation preferable over direct

search to the client. However, since sc has a constant

monotonic effect on UiD, we set sc to 0 for the

simulations in this paper. Further evaluation of the

influence of sc on the intermediary’s revenues will be

done in future research.1

As pointed out earlier, the switch point for the

search rule in our model can only be characterized in

terms of a functional equation that cannot be solved

explicitly. Therefore, a closed-form solution to iden-

tify the clients that will use the intermediary is, in

general, not possible. In situations where it is not easy

to obtain closed-form solutions from a model,

simulations have become a commonly used technique

in management research and extensive models have

been developed for specific business applications.

Simulations also help to isolate the impact of specific

constructs under study for detailed examination. For

example, Rivkin [25] used simulations to study the

impact of the complexity of strategies on their

duplication by competitors. Simulations have also

been used to examine team processes [6]. We used a

simulation model to study the selection process and

identify the utilities for each participant in the

marketplace as described in the setup below.

4. Simulation results

4.1. Setup

In the simulations, for each client, we compare the

utility from direct and mediated search to identify the

market the client will prefer. When the client selects

the intermediary, it pays the intermediary fee and uses

the intermediary to facilitate search. Intermediary

revenues are calculated by summing client fees for

those clients that prefer to use intermediary services.

When direct search completes, clients use Eq. (3) to

evaluate their utility UiD from direct search. Clients

compare this to the utility UiI they would derive from

using the intermediary. This information is provided


to clients by the intermediary using Eq. (2) at the

beginning of the search (though the identity of the

vendor is not revealed). If UiI exceeds UiD net of fees,

the client pays the intermediary fee and contributes to

intermediary revenues. Intermediary revenues R are

calculated by summing the fees from clients that

choose the intermediary as in Eq. (4).

R ¼Xi

Pint ð4Þ

To examine the effect of intermediary fees, we

calculated R for intermediary fees Pint ranging from 0

to u, the maximum price clients was willing to pay for

the service. The revenue-maximizing fee and revenues

are identified from the resulting revenues as shown in

Section 4 for the different configurations. We ran

many different search configurations to examine the

revenues and revenue-maximizing fees of matching

intermediaries.

In the simulations, we introduced 1000 clients and

recorded the mean values per client of the various

parameters such as intermediary revenues over these

1000 clients. The simulations were then repeated 100

times to gather observations over 100,000 independ-

ent client observations. The mean values over these

100,000 observations have been reported here to

ensure the robustness of the results [16]. A separate

simulation was run for each size of the population of

vendors in the marketplace. The service specifications

of each vendor and client were generated randomly

from a uniform distribution around the circle. When

consumption was allowed, clients used the services

provided by the best-known vendor found in earlier

searches. Since we consider horizontal differentiation,

all clients had the same reservation value (willingness

to pay). The cost of mismatch c, and the client

reservation value were such that when the client and

vendor were on opposite sides of the diameter on the

circle, the client obtained no utility from the service

offered by the client.

We started with a random search model. This

provides a benchmark for the profitability of an

intermediary because it examines the impact of the

most rudimentary direct search strategy on interme-

diary revenues. We then introduce deliberate search

and examine the impact of consumption during search

on the profitability of the intermediary. Finally, we

compare the case where clients repeatedly enter the

market to search for services. In the simulations,

service specifications of vendors were uniformly

distributed around the circumference of the circle

though this information was not available to the

clients.

4.2. Random search

We start with a basic comparison of mediated and

direct search. Consider the case where there are two

time periods (T=2), the first of which is spent in

search and evaluating a vendor and the second is spent

in using services from the vendor identified in the first

time period. Since only one vendor is evaluated at a

time in sequential direct search, this case may be

considered a zero intelligence search where the

optimal vendor (identified by the intermediary) is

compared to the first random provider identified by

direct search. In Figs. 3 and 4, we plot the

intermediary’s revenues as a function of mediation

fees Pint charged per client for different sizes of the

vendor population. Fig. 3 shows intermediary rev-

enues when random search is possible and Fig. 4

shows intermediary revenues when the only option to

clients is mediated search.

These figures indicate that there are distinct differ-

ences in the intermediary’s business performance when

the simplest form of direct search is considered in the

analysis versus when it is not. Direct search has two

main effects on the intermediary’s revenues. The first is

that when direct search is considered, there is an

optimal fee that an intermediary may charge clients for

matching services. In the absence of direct search, the

intermediary is able to charge a fee that is very close to

the clients’ reservation value and extract virtually all

the surplus of the client from the use of the service

offered by the provider. In smaller markets (10 vendors

in Fig. 4), the intermediary’s optimal fee is less than the

reservation value simply because even the best vendor

for a client still does not generally provide the ideal

selection of services for the client. In the presence of

direct search, the optimal fee is close to one-third the

value of the fee in the absence of direct search. The

second is that the intermediary’s revenues/client fall

sharply when direct search is allowed. On our scale,

revenues fell from almost 180 units to about 25 units at

the point where the profits are maximized when direct

search is considered (at a mediation fee close to 60).

Fig. 3. Intermediary revenues in the presence of random search.


When direct search is allowed, it is seen that the

intermediary’s revenues are low for very low and very

high unit fees and are highest for intermediate fees.

This is explained by the fact that when the unit fees

are low, net utility for a large majority of the clients is

higher after paying intermediary fees and availing

matching services than after direct search. It may be

noted that this direct search is not intelligent in the

sense that clients only compare a random provider

with the most preferred supplier in the population.

Low mediation fees imply that the benefits of

matching are not diminished greatly by the matching

fees. As a result, most clients avail themselves of the

intermediary’s matching service but the intermediary

earns very little from each client. On the other hand,

when the intermediary charges very high fees, much

of the value of matching is eroded by the bmatching

taxQ levied by the intermediary. Under these con-

ditions, the only clients that still find it efficient to use

the intermediary are those whose luck of the draw was

rather unfavorable and the service specifications of the

selected provider yielded very little utility for the

clients. As the fees were increased, there were fewer

and fewer of these clients and consequently, though

each such client is highly profitable to the interme-

diary, the overall effect is a decrease in the overall

profitability of the intermediary. When the fees are

almost equal to the clients’ reservation prices, there

are no more clients for the matching service and the

intermediary’s revenues reduce to zero. There is

therefore an optimal intermediate price at which the

intermediary achieves the highest profitability.

On the other hand, when direct search is not

explicitly considered, the intermediary’s revenues are

monotonically increasing in fees for all but the

smallest populations of vendors. This is explained

by the fact that clients prefer to use the intermediary

as long as the utility derived from the preferred vendor

net of matching fees is positive. When the population

of vendors is small, the utility from the most preferred

vendor could be significantly less than the reservation

price and, depending upon the specific configuration

of the vendor service specifications, many clients may

not derive positive utility after accounting for match-

Fig. 4. Intermediary revenues when direct search is not considered.


ing fees. This indicates that when direct search is not

an option, the optimal policy for an intermediary

depends upon the size of the vendor population. For

smaller populations, there is an optimal matching fee

that maximizes intermediary revenues. For larger

vendor populations, the optimal pricing policy is to

charge a fee close to the clients’ reservation price and

extract maximum revenues from the intermediary’s

knowledge of the supplier population.

It must be remembered that the model here is very

favorable to the intermediary. Direct search is nothing

more than a random draw from the vendor population.

Yet, the change has significant effects both on the

overall revenues of the intermediary and the pricing

policies of the intermediary.

4.3. Consumption during search

We now consider the case where clients use a

search rule to terminate sequential direct search. The

search rule used in this research is based on Roths-

child [26]. In such a case, it is necessary to distinguish

between services with low and high switching costs.

When switching costs are low, clients are likely to

consume services from providers even while search-

ing. On the other hand, when switching costs are high,

clients are likely to forego consumption of services

until search terminates. In such cases, utility from

consumption during search could be reduced signifi-

cantly by switching costs so that it is efficient to wait

for search to complete before consumption.

To simulate low and high switching costs, we

expanded the time horizons in Eqs. (2) and (3) to 16

time periods (T=16). We consider two limiting cases:

where there is no switching cost for the client and

where there are prohibitively high switching costs.

The first time period is used exclusively for search,

but in the case of services with low switching costs,

starting with the second time period, clients begin to

utilize the services of the best available vendor,

switching providers as superior providers are identi-

fied until search terminates. Upon termination of

search, clients start using the services of the best

available vendor in both cases of search with and

without consumption. Intermediary revenues are

calculated as before by summing the revenues gained

from the clients that select mediation. Fig. 5 shows the

intermediary’s revenues per client for both cases under

different sizes of the vendor population. The labels for

each curve indicate the size of the vendor population

Fig. 5. Consumption versus non-consumption during search.


and whether consumption was possible or not. For

example the label n_cm10 indicates no consumption

and 10 vendors and cons100 indicates 100 vendors

and consumption during search. To enable compar-

ison between cases, all fees and revenues have been

normalized on a per-time-period basis.

It is again seen that the profits for the intermediary

are highest for some intermediate values of client fees.

Profits are low for very low and very high fees. It is

also seen that profits are higher when consumption is

not done during search compared to cases where

consumption is done during search. The difference

between the two cases lies in the fact that when

consumption was allowed during search, clients were

deriving some positive level of utility of service even

while searching. These results indicate that a matching

intermediary is likely to be most profitable in cases

where switching costs are high and consumption is

not viable during search.

4.4. Search experience

Thus far, we have only considered the case of

clients who enter the market for the first time.

However, as clients perform direct searches, they

gather information about the marketplace and are able

to make more informed decisions in subsequent

searches. In particular, clients can form beliefs about

the minimum utility that may be expected from direct

search and can reject vendors whose service specifi-

cations are such that the utility from using their

services is below the belief level. This reflects the

client’s updated knowledge about the distribution of

vendors in the market. The interesting feature of this

model is that it allows us to consider the influence of

the obsolescence of information gathered in past

searches on current search.

Information gathered from earlier searches even-

tually becomes obsolete due to a number of factors.


Demand and supply conditions change over time and

further search is required just to ascertain the new

vendor characteristics and prices. In dynamic indus-

tries, where technologies change rapidly, product and

service features change over time. Finally, every

market has a component of randomness created by

the changing identity of buyers and sellers. The initial

ignorance of new buyers and sellers makes the

information of even the most experienced participants

obsolete [32].

The obsolescence of information gathered from

prior search is modeled by specifying the rate at

which client’s expectations regarding the utility

from direct search decays with time. Clients lower

their expected utility from direct search in propor-

tion to the rate of decay of information. Thus if

client utility per time period achieved from direct

search in iteration 4 in a certain search for a cer-

tain client is 150 and the rate of decay is 0.8, the

client will lower its expectation from direct search

to 120 and will reject all vendors in iteration 5

which yield utility per time period less than 120.

To examine the influence of repeated searches and

belief levels, we extend the simulations to perform

15 iterations of direct search. In each iteration,

while performing direct search, clients rejected

providers who yielded utility lower than the

expected utility. The viability of intermediation

under different conditions of information decay

becomes clearer when we examine intermediary

Fig. 6. Intermediary revenue

revenues/client/time period for different belief

levels under consumption and non-consumption.

Intermediary revenues are calculated using Eq.

(4). Fig. 6 below shows intermediary revenues/

client/time period after 15 iterations of search. To

facilitate comparison, only the case of search without

consumption is shown in the paper, the results for the

case with consumption are similar.

The graphs show intermediary revenues when

clients are assumed to search without consumption

for belief levels of 1.0 and 0.8. Comparing the two

graphs, we see that as expected, intermediary

revenues are greater when the rate of obsolescence

of information gathered during search increases. It is

also seen that intermediary revenues increase when

the size of the vendor population increases, though at

a decreasing rate. The increase in intermediary

revenues when the vendor population changes from

10 to 100 is much greater than the change in

revenues when the vendor population increases from

100 to 1000.

Since the intermediary’s revenues in our model are

obtained exclusively from fees paid by clients, we

tried to understand the underlying phenomena that

were driving intermediary revenues by calculating the

utilities of clients under direct and mediated search as

they gained experience from the search process. As

the clients completed search iterations, we computed

the cumulative utility of clients from all prior

searches. A plot of these cumulative client utilities

s for repeated search.


for the different configurations studied in this paper

are presented in Fig. 7 below.

The two sets of graphs show the average cumulative

utility for a set of 1000 clients from mediated search

and direct search over 15 iterations for two belief

levels. The upper graphs are for the no-consumption

cases and the lower graphs are for the cases where

consumption of services is allowed. The LHS graphs

represent belief levels of 1 and the RHS graphs

represent belief levels of 0.8 (degradation of informa-

tion). The labels for each curve indicate the number of

vendors, intermediary fee and if the search was direct or

mediated. For example, v10_ fee24D indicates that

there were 10 vendors, intermediary fee was 24 units

Fig. 7. Cumulative client utilitie

and search was direct. The figure shows that when

belief is 1.0, i.e. clients reject all vendors who are less

satisfactory than the best vendors found so far, utility

from direct search rapidly surpasses that frommediated

search both when consumption is allowed and when it

is not. However, the graph on the top-right shows

(v200_ fee24D and v200_ fee24I) that when the beliefs

are lower than 1.0, vendor fees are low and the

marketplace includes a large number of vendors,

mediated search is generally superior to direct search.

Belief levels of 1.0 indicate that the market is fairly

static and clients can be confident of evaluating

vendors based on their knowledge of the marketplace

gathered in previous searches. In such cases, it is seen

s after repeated searches.


that it is very efficient for clients to invest in gathering

knowledge about the marketplace through direct

search and avoid paying mediation fees. Though

client utilities are low in initial searches and the use of

mediation might appear efficient at that stage, the

knowledge gathered about the market pays back very

rapidly and in as few as seven or eight iterations,

utility from direct search exceeds that from mediated

search, even when the intermediary charges a very

low fee. Therefore, we conclude that when the

markets are relatively static and clients can use the

experience they gather from previous searches in

future searches, it is efficient for them to avoid the use

of matching services altogether. However, when the

marketplace is dynamic with client needs or vendor

offerings change rapidly with time, clients cannot be

confident of the value of the knowledge they gather

from earlier searches and are forced to consider more

vendors than their counterparts in static markets. This

may happen when vendors are constantly upgrading

features of their services or when client needs are

changing rapidly. A matching intermediary can offer

significant benefits in such dynamic markets, by

matching clients to vendors.

Finally, we summarize the results found in this

paper in Table 1, which shows the optimal fees of the

intermediary and its corresponding revenues for the

four configurations examined in this paper.

Table 1

Intermediary’s optimal fees and profits

(a) Random search versus mediated search

Two time periods, one iteration

Vendor population 10 100 100

Direct search allowed? yes no yes no yes

Intermediary’s optimal

fees/time period

54 138 60 174 60


revenues per time period

19.31 130 26.2 168 26.7

(b) Repeated search

T=16; 15 iterations; belief=1.0

Vendor population 10 100 100

Consumption allowed? yes no yes no yes


fees/time period

36 30 24 36 24


revenues per time period

0.3 1.3 0.9 2.14 0.93

Table 1(a) shows that the intermediary’s profits are

highest when no direct search is allowed. At the

optimal fees, its profits are higher when consumption

is not allowed during search. Also, the intermediary’s

profits increase as the vendor population increases in

all columns. When clients consider search horizons

that extend to future searches, the intermediary’s

profitability decreases sharply as seen in Table 1(b),

though increasing rate of obsolescence of information

helps improve the intermediary’s revenues profits.

An interesting observation may be made regarding

the superiority of mediation in dynamic markets in

which the characteristics of vendor services change

frequently and clients are unable to extrapolate their

expectations from prior searches to future searches. In

many cases, such dynamic markets have been created

by the creative use of information using the vast

amount of computing power now available in many

companies. For example, in the market for airline

tickets, prices change very rapidly and different price

combinations are created in real time by the informa-

tion systems of these companies. As a result, customers

cannot hope to repeatedly get comparable prices for the

same trip specifications. On the other hand, online

intermediaries can easily integrate and process this

information, keeping information-acquisition costs

under check. The divergence between client utilities

for mediated search and direct search over iterations in

0

no

174

174

T=16; 15 iterations; belief=0.8

0 10 100 1000

no yes no yes no yes no

12 12 6 12 24 12 24

2.58 0.53 4.85 3.45 13.44 4.06 15.21


the presence of information obsolescence in Fig. 7

suggests that matching intermediaries such as those for

airline tickets could be viable in the long run.

5. Conclusions and discussions

The research found that the introduction of direct

search significantly reduces the revenues earned by

the intermediary, even when clients have no prior

information of the distribution of vendor features.

When direct search is allowed, there is an optimal fee

which the intermediary can charge to maximize

intermediary revenues. The intermediary revenues fall

when clients are allowed to consume services while

searching compared to the case where consumption is

not allowed during search. When client information

atrophies rapidly between searches, it becomes more

efficient for clients to use the intermediary and

therefore, intermediary revenues increase. These

results are also intuitively reasonable because the

intermediary’s revenues depend upon the surplus that

may be extracted from the expected mismatch

between clients’ optimal service specifications and

the service specifications of the selected vendor in

direct search. The intermediary’s revenues are found

to increase as the expected mismatch increases. The

research can be used to generate many testable

hypotheses. For example, it is expected that (1) inter-

mediaries should be more profitable in markets

characterized by information obsolescence, (2) inter-

mediaries should be more profitable in markets where

switching costs for services are high.

The results indicate that matching intermediaries

can offer the greatest benefits in markets where the

value of information gathered in search atrophies

rapidly. In such cases, intermediary profits may be

protected if knowledge of prices and services offered

in prior searches is inadequate for clients to filter out

vendors. The viability of intermediaries in such

markets must however be tempered by at least two

concerns. The first is that the profitability of an

intermediary in such markets would depend upon the

efficiency with which it can gather all the required

information about all vendors in the first place. If

information-gathering costs for the intermediary

exceed the differential utility of the matching service

to clients whose willingness to pay for the matching

information will be constrained by the utility they can

gather from direct search, intermediaries would not be

profitable even in the markets most favorable to them.

The other concern would be the role of competition

among intermediaries. This research has examined the

revenues of a monopoly intermediary and the intro-

duction of competition will constrain the profitability

of the intermediary further.

This last observation points to an important direc-

tion for future research. Intermediaries are likely to

achieve significant economies of scale in providing

matching services, thereby achieving profitability.

However, to protect their profits from being eroded

by defecting clients and competition, intermediaries

may also attempt to provide other value-added services

that may not necessarily offer economies of scale. One

such possibility is the monitoring of quality [5].

One limitation of this research is that the simu-

lation is based on a combination of an optimal search

design for each individual search and a mechanism

design for repeated search. Though the search

algorithm used in direct search is based on Rothschild

[26], who proved the optimality of the algorithm, the

repeated searches are based on a mechanism design of

transfer of expected utilities from previous searches

adjusted for the likelihood of changes in market

conditions. It is possible that a superior mechanism

design for repeated search can further reduce the

utility of mediated matching.

Acknowledgements

The paper is based on the PhD dissertation of the

first author. The feedback from participants at the

AMCIS 2001 meetings in Boston, MA is gratefully

acknowledged. The paper has benefited significantly

from the comments of two anonymous reviewers. An

earlier version of this paper was selected for a best

paper award at the AMCIS 2001 conference. We

thank Prof. V. Sambamurthy and Prof. R. Garud for

helpful comments during the AOM 2000 meeting in

Toronto. This research has been funded by the

National Science Foundation under grant 9907325.

Any opinions, findings, and conclusions or recom-

mendations expressed in this material are those of the

author(s) and do not necessarily reflect the views of

the National Science Foundation.


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Manish Agrawal is an Assistant Professor in the department of

Information Systems and Decision Sciences at the University of

South Florida in Tampa. He completed his PhD at SUNY Buffalo.

His research interests include Information Systems outsourcing,

electronic commerce and electronic intermediaries. His articles have

appeared or have been accepted for publication in Decision Support

Systems, the Journal of Organizational Computing and Electronic

Commerce, Communications of the ACM and his work has

received the best paper award at the proceedings of the Americas

Conference on Information Systems, 2001.


Govind Hariharan is an Associate Professor at the Coles College of

Business of the Kennesaw State University. He specializes in the

Economics of Regulations, Health Economics and Information

Economics. Dr. Hariharan has taught business economics courses

primarily for the executive MBA programs in Buffalo, Beijing and

Singapore and Health Care Systems and Health Economics courses

in the Health Care Administration program. He has received

numerous awards for his teaching. He has assisted many technology

start-ups get off the ground and has consulting experience in the

banking, health care and grocery industries and has also served on

numerous health-related task forces.

Rajiv Kishore is an assistant professor in the School of Management

at the State University of New York at Buffalo. His research is

focused on contemporary techno-organizational innovations geared

towards improving an organization’s IT services delivery capabil-

ities, and has been published or accepted for publication in

Information Systems Frontiers, Communications of the ACM, and

Journal of Healthcare Information Management. Rajiv has consulted

in some of these and related areas with a number of large

companies, some of which include BellSouth, Blue Cross Blue

Shield of Minnesota, IBM, and Pioneer Standard Electronics. He is

also the recipient of a multi-year National Science Foundation

research grant as a co-principal investigator for conducting research

in the area of IT outsourcing.

H.R. Rao is a Professor in the Department of Management Science

and Systems at SUNY Buffalo and Adjunct Professor in CSE. His

interests are in the areas of management information systems, e-

business and outsourcing. He has authored or co-authored more

than 90 technical papers, of which more than 60 are published in

archival journals including Management Science, Information

Systems Research and MIS Quarterly. His work has also received

the best paper runner-up award at ICIS 2003. Dr. Rao has received

funding for his research from the National Science Foundation, and

he received the prestigious Teaching Fellowship at SUNY Buffalo.

He is a co-editor of a special issue of The Annals of Operations

Research, the Communications of ACM, Decision Support Systems

and co Editor-in-Chief of Information Systems Frontiers and

Associate Editor of ISR, IEEE SMC and DSS.

matching intermediaries for information goods in the presence of direct search: an examination of...

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