APPLIED ECONOMICS WORKSHOP
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
APPLIED ECONOMICS WORKSHOP
Business 33610
Spring Quarter 2009
Fiona Scott Morton
Yale School of Management
(Jorge Silva-Risso, Florian Zettelmeyer, Victor Bennett, & NBER)
"The Interaction of Technology, Organization, and
Product Market Strategy: The Case of Auto Dealerships"
Wednesday, June 3, 2009
1:30 to 2:50pm
Location: HC 3B
For any other information regarding the Applied Economics Workshop,
please contact Tamara Lingo (AEW Administrator) at
773-702-2474, tammy.lingo@ChicagoBooth.edu, or stop by HC448.The interaction of technology, organization, and product market
strategy: The case of auto dealerships ∗
Fiona Scott Morton
Yale University and NBER
Jorge Silva-Risso
University of California, Riverside
Florian Zettelmeyer
Northwestern University and NBER
Victor Bennett
University of California, Berkeley
May 2009
Extremely preliminary. Comments welcome.
∗
We are very grateful to seminar participants at Kellogg for influencing the direction of this paper. In
particular Tom Hubbard, Shane Greenstein, and Scott Stern provided valuable comments. We also thank
Brian Viard for his input. Scott Morton and Zettelmeyer gratefully acknowledge the support of NSF grant
SES-0111885. Corresponding author: fiona.scottmorton@yale.eduThe interaction of technology, organization, and product market
strategy: The case of auto dealerships
Abstract
We study the benefit to automobile franchise dealer from adopting website tech-
nology. We demonstrate considerably heterogeneity in the strategies adopted by
auto dealers. Our evidence shows dealers adopt clusters of policies: product mar-
ket strategies, internal organization and incentive policies, and technology adoption
policies. A simple model demonstrates that dealers that adopt web technology will
tend to have lower margins and customer-friendly organizational practices. Because
we show that web capability is intrinsically correlated with other dealership policies
through the dealer‘s strategy, we instrument for technology adoption. Our instru-
ments include demographics of the county and census block of the dealer, as well as
dealer characteristics such as nationality of nameplate. An IV regression shows that
web technology significantly predicts larger dealer volume and lower dealer margins
on new cars. Our results are stronger when we focus on the combined characteristic:
the dealership adopts web technology and also implements appropriate job design
for its internet sales staff.1 Motivation
We study the benefit to automobile franchise dealer from adopting website technology. We
demonstrate considerably heterogeneity in the approaches adopted by auto dealers in their
effort to sell cars effectively. Our evidence shows dealers adopt clusters of policies. These
clusters include characteristics of the dealer’s product market strategy, internal organization
and incentive policies, and technology adoption policies. We refer to these clusters as “dealer
strategies.” For example, a dealer that sells new cars in high volume and at low margins is one
type of strategy we see in the data. Another strategy relies on high profits from financing and
more sales of used cars. These product market strategies are associated with different human
resource and organizational choices, e.g. the large new car dealer does more inhouse training
and the financing/used dealer bases compensation more heavily on gross margin. Inputs such
as the quality of the dealer‘s building also vary by strategy. And of course, choice of technology
is determined in conjunction with product market and other strategies. The high volume-low
margin dealership has more website capabilities than the other. Later in the paper we elucidate
these choices using a simple model of complementarities among policies. In our model, dealers
that adopt web technology will tend to have lower margins and customer-oriented organizational
practices. Because we show that web capability is determined jointly with other dealership
policies through the dealer‘s strategy, attempting to explain sales or margins with a measure of
technology will not generate an estimate of the causal relationship. We will use an instrumental
variables strategy to determine the relationships between technology adoption, volume, and
margins.
Technology and organization
A dealer‘s website (technology, in our setting) allows a dealer to be found by a consumer
searching with a search engine. Once a consumer arrives at the website, she becomes a “lead,”
or a “prospect.” She looks at the information on the website and decides whether or not to
visit the dealership or contact it by phone or email. Dealers can put contact information, such
as email addresses and phone numbers on their sites; they can allow past customers to schedule
repairs; they can allow prospects (who may become customers if they decide to buy) to view
dealer inventory of vehicles; they can provide driving directions and hours of operations; and
they can allow a prospect to make an appointment with a salesperson. However, to get the full
benefit of visits to the site, the dealership must make appropriate organizational change within
2the dealership. For example, if no one is made responsible and given incentives for answering
email in a timely fashion, messages will not be answered, and prospects will buy elsewhere.1
Some dealerships will have a dedicated person to answer email enquiries, some will have an
Internet department, and some will have an Internet service manager, or ISM. Additionally,
an even bigger productivity boost may occur if this ISM has the optimal skills for the job, for
example, the ability to effectively use both email and the phone. One motivation for this paper
was the experience of an author who, several years ago, attempted to schedule an appointment
at the website of her VW dealership. The website had a place to enter a phone number which, it
said, would cause the service department to telephone to schedule a repair. The author never
received a return phone call. This experience illustrates that dealerships may not (at least,
right away) make the organizational changes needed to take advantage of a new technology.
2 Literature review
For years the Economics literature has attempted to elucidate the three-way interdependence
of a firm’s organizational form, technology use, and product market strategy. Unfortunately,
because of the rarity of matched data on all three, most work has focused on interactions
between two of the three.
For the interaction of information technology and organization, a recent literature has
emerged relying on estimating production functions. The papers in this group have typically
discovered a complimentarity between information technology use and organizational form.
Bresnahan, Brynjolfsson, and Hitt (2002) carry out a survey of work organization at a
sample of large firms by asking senior human resource managers both to provide information
about the types of workers at the firm (education, etc) and to rate firm practices in areas like
team-building and worker control over work practices. The paper shows positive correlation
between work organization and the amount of IT (stock of computers) utilized by the firm.
The paper also estimates production functions including interactions between IT and work
organization, and finds this interaction has a positive impact on productivity.
Bloom, Sadun, and Van Reenen (2007) also consider the question of the effect on productiv-
ity of the interaction between organization and IT (PCs per employee). They examine a sample
of UK establishments that were acquired either by US, other multinational, or domestic firms
in the 1990s. The US-owned establishments showed higher use of IT and more productivity
1
Anecdotally, a response time under 20 minutes for price quote enquiries significantly increases sales.
3per unit of IT.
Another branch of this literature forgoes production function estimation and attempts to
measure the causal effect of information technology on organizational form. One example,
by Acemoglu, Aghion, Lelarge, Van Reenen, and Zilibotti (2007) models firms experimenting
with technology and demonstrates that firms closer to the technological frontier have shallower
organizational structures.
All three of these papers have the advantage of a great deal of generalizability from their use
of multiple industries. This approach, however, has two main drawbacks. The first being that
IT stock must be measured at a very general level: for example, the value of the computing
stock or the number of PCs per person in the firm. The second limitation is that product
market strategy is difficult to measure across such disparate firms, so strategic changes that
occur with technological adoption of organizational change cannot be identified or their effects
correctly attributed.
In contrast, work by George Baker and Tom Hubbard (Baker and Hubbard 2003, Baker
and Hubbard 2004, Hubbard 2003) analyze a case in which technology serves as a substitute
for certain organizational tasks, like managerial monitoring. In this work the authors study
the effects of IT (on-board computers) and argue convincingly that the monitoring function of
the technology replaces the incentives created by truck ownership for the truck driver, while
the coordination-enhancing abilities of the technology make outsourcing of shipping activities
more efficient. This work uses waves of a trucking census to demonstrate that adoption of this
type of IT resulted in organizational change: more vertical integration into truck ownership
and less vertical integration into shipping. The nature of the truck census data does not allow
for an examination of product market strategy.
A second literature, studying the interaction between technology and product market strat-
egy includes that of Bartel, Ichniowski, and Shaw (2007). This paper examines the impact of
the adoption of new technology in a dataset of valve plants. The authors survey plant man-
agers to learn about the technology and product market strategy at the time of the survey.
They also ask the managers to answer the same questions for the plant five years prior to the
survey. This yields a dataset that is similar to a panel, though not quite as clean due to the
strong time trends in technology and product market strategy in this industry. Nonetheless,
the authors find that plants that adopt advanced computerized machine tools have higher la-
bor productivity and shift their product mix toward more customized products, which have
become less costly to produce due to the new machines. Though the authors note that higher-
4technology plants also demand more skill in workers due to their operation of the more complex
machines, they do not observe any commensurate change in organizational form, like compen-
sation structure, hierarchy, or job design. The authors do not address the causal relationships
between technology and product mix and labor. Demand for customized products might lead a
firm to purchase machines or purchasing machines might lead a firm to make more customized
products.
The single industry focus of many of these papers opens the door to more specific and
comparable measurement of information technology and product market switches, but has the
disadvantage of less heterogeneity in organizational form.
For the third pairing, organization and product market strategy, the literature has sug-
gested that firms operating in more competitive product markets take on shallower organiza-
tional forms. Guadalupe and Wulf (2008) use changing tariff rates to measure increases in
competition and demonstrate a flattening of firms. As with the first set of literature, this mea-
surement yields a great deal in terms of generalizability, but requires very coarse measurement
of organizational form. Furthermore, changes in organization due to technological adoption
spurred by competition are attributed directly to competition.
Our paper aims to convincingly address the interaction of all three of organization, infor-
mation technology, and product market strategy. In order to do so, we focus on one industry,
new automobile sales. This narrow focus allows us to measure a very specific form of Informa-
tion Technology and therefore, we hope, get something reasonably precise. We analyze only
one piece of technology, the dealer‘s web page, and its attributes. Furthermore, the limited
variations in organizational form amongst dealerships allows us to divide up types of firms in
a reasonable way and link other attributes to variation in form. The third main benefit of this
narrow focus is the ability to observe and identify changes in product market strategy while
controlling for confounding changes in competition. We can quantify product market strategies
such as markups, price discrimination, and sales of complementary products.
We refer readers interested in the economics of auto retailing to our own previous work
(Scott Morton, Zettelmeyer, and Silva-Risso 2001, Scott Morton, Zettelmeyer, and Silva-Risso
2003, Zettelmeyer, Scott Morton, and Silva-Risso 2006). In these studies we examine the effect
of shopping online, race and gender discrimination, and how consumers search and bargain for
a new car. These papers also describe the industry and the institutional environment of auto
retailing.
53 Industry Background
3.1 Production Function
The production function for auto retailing is extremely simple. It requires an inventory of cars,
a building, and a group of salesmen who match potential customers with cars and bargain over
price with the customer. However, within this basic structure, an auto dealer must choose
particular policies that create variation across franchises. Charging low margins to attract high
volume is one such choice. Others might be to incur the higher selling costs associated with
price discrimination, or charge high margins and accept lower sales as a consequence. Earning
profits from selling financing and insurance is another area a dealer can emphasize. Financial
incentives for salesmen (volume, margin, services), training, and monitoring practices such as
tracking leads must be selected by the sales manager.
3.2 Adoption
Dealerships choose if they will adopt a website. The adoption of the technology will be made
by dealerships that expect to gain the most from it. A website may attract a particular type of
customer (low margin, more informed, younger) and may therefore be more or less attractive
depending on the strategy of the dealership. The website may also allow for monitoring of leads
coming in to the dealership, which may or may not be a match for existing dealership practices
concerning leads.
Nameplates (e.g. Chevrolet, BMW, Toyota) are an important source of influence on adop-
tion of IT. They can endorse a software product by saying it is approved for franchisees. Often,
multiple competing products are approved in this way. Or, a nameplate can endorse a set of
products and include substantial incentives for dealers to adopt. (Franchise laws prevent name-
plates from requiring adoption of any technology policy by a dealer.) These incentives typically
require a dealership to have the technology in order for it to attain some special status with
the manufacturer, which in turn earns the dealership privileges (e.g. Chrysler 5-star dealer or
Lexus “Elite” status). An autogroup of multiple franchises with a single owner may invest in
a website platform that it offers at low marginal cost, or requires, for its members.
3.3 The role of the franchise system
Franchise territories are strictly governed by the franchise agreements between the auto fran-
chisee and the manufacturer (franchisor) and state laws. Franchisees can object to any change
6the manufacturer might make that would reduce expected profits, such as granting another
franchise nearby or changing the location of an existing franchise. Furthermore, US-based
automakers established dealer networks when the optimal number of dealers was larger due
to higher market shares of US nameplates and lower economies of scale in repair operations.
Additionally, populations grow and move (e.g. from the city to the suburbs) and so a dealer
network designed for consumers 50 years ago is no longer in the right place. Population growth
has varied considerably across the counties in our sample; Pennsylvania has some counties that
shrank while the west coast has many very high growth counties. Thus, any configuration of
dealerships that was established long ago is no longer optimally designed relative to demand
today. One can see evidence of this in the current auto crisis. Bankruptcy is giving American
nameplates the chance to terminate dealerships without the high costs of compensating the
dealerships with the present discounted value of their rents, and they are taking advantage of
that option. Non-American nameplates (e.g. VW, Toyota, Honda) have many fewer dealerships
which were located more recently, and therefore we assume are matched more closely to cur-
rent demand. We exploit these differences across nameplates and counties in our instrumental
variables strategy.
3.4 The Impact of Organization
A fundamental choice facing a sales manager is how to configure the organization to handle
the leads arriving from the new source, the website, as well as any other channels. The sales
manager may simply assign the leads to a showroom floor salesperson he thinks is competent
and let him handle them along with his other work. Alternatively, the dealership might set up
an Internet department and assign salespeople who have to respond to email leads with email
and phone selling. An Internet Sales Manager, or ISM, is the name for a salesperson who heads
this department. Because the mix of skills that is desirable for a salesperson to have may be
different across the two jobs, the dealership might set up an Internet department and hire new
staff with the particular skills needed to work in it, rather than simply deploying floor salesmen
to handle Internet customers. The survey asks questions about who is assigned to which jobs
and about how leads are tracked.
Compensation is also a critically important issue in auto retailing. The usual compensa-
tion scheme for a salesperson is a percentage of the gross margin on a vehicle (less a constant
subtracted off by the dealership). Internet salespeople may be compensated differently. For
example, Autobytel.com requires that salespeople handling leads from its website be compen-
7sated on volume, not margin. (We have no knowledge of how many dealerships adhere to this
rule.) The Internet Sales Manager’s typical compensation is a combination of some percentage
of the net sales in his department plus a function of customer satisfaction.
3.5 Managerial input
We held conversations with a number of managers at a firm that sells software to dealerships
(Cobalt) as well as other industry experts and dealers in order to determine what practices
impeded productivity gains for dealerships that had adopted websites. Based on those conver-
sations we formed several hypotheses about the sort of organizational changes dealers may need
to make to gain maximum productivity out of the software. We developed survey questions
that measure the organizational practices at the dealership. We focus on practices we think
are particularly productive once leads are coming in to a dealership from its website. Below we
describe the organizational problems and then the associated questions.
Dealerships typically allocate customers who come on the lot to whichever salesperson
greets them first. Thus, a salesperson who is also supposed to be answering email queries
via the website faces a conflict between waiting on the front steps in order to sell to walk-in
customers and spending time at his desk sending email. Allocating leads from the Internet and
creating incentives to respond to them quickly is one organizational issue for dealerships when
there is not a dedicated Internet Sales Manager (ISM).
A feature of the Internet that is different from walk-in leads and even phone leads is that
it is easier to accurately measure website leads and track them. The dealership has an interest
in following up with every possible lead because some of those leads will buy today, and others
may be ready to buy in the future. These future sales are more likely if the salesman creates
a favorable impression and obtains some contact information from the customer. However,
the salesman is focused on leads that will convert today, e.g. the ones that walk onto the lot.
The interests of the salesman and the dealership do not perfectly coincide because there is
considerable turnover among sales staff: 30-50% a year is not unusual. Thus the salesperson is
not likely to put as much effort as the dealer would like into prospects that appear less likely
to buy today. Additionally, regardless of the source of the lead, salesmen have an incentive
to under-report the number of prospects at the dealership because their performance is essen-
tially the ratio of sales to prospects. Accurate counting of leads coming in via the website
and other channels becomes a measurement tool which can be used to create incentives and
compensate salespeople. We ask several questions about the extent and type of lead-tracking
8at the dealership.
The ISM typically contacts the lead and attempts to arrange an appointment with a sales-
person at the dealership. Since both the ISM and the salesperson who closes the sale are critical,
the right compensation scheme that gives the two parties good incentives must be designed.
Creating a compensation scheme both in terms of tasks and in terms of what is measured and
rewarded is an important organizational issue for dealerships. The survey asks managers about
the basis of compensation for several job types.
Finally, the traditional salesperson is skilled at selling to customers who walk into the
showroom. Responding to website inquiries requires communicating effectively and persuasively
by email and over the phone. Thus the mix of skills that is desirable for a salesperson to
have may be different once there is a steady stream of leads coming through the Internet.
Alternatively, the type of customers using the Internet may be different from the norm (perhaps
more informed) and so the salesperson assigned to them will be more effective if he can address
their different needs. Internet sales staff might benefit from training that is particular to the
medium they use. The survey asks about job description, training, and compensation.
4 Model
An advantage of studying a particular technology is that we can write down a model that reflects
the role of that technology in the activities of firms in our industry. Web-based communication
between car dealers and potential customers allows customers to do a number of things. For
example, customers can obtain information about the dealership, make an appointment, search
inventory, or get a price quote on a particular car. These activities increase the efficiency of
car-buying for the customer. The customer saves time and disutility of shopping by using the
Internet (Zettelmeyer, Scott Morton, and Silva-Risso 2006). The interesting problem for the
researcher is that this sort of efficiency gain will not (fully) show up in GDP, nor in measures
of revenue or productivity of the dealership. Unmeasured efficiency gains are an important
characteristic of the use of the Internet in retailing and other settings (Scott Morton 2005). A
consumer clearly gains when it is easier to locate a store, determine opening hours, identify a
product of interest etc. Competition from other retailers who also adopt the same technology
prevents the retailer from capturing the full benefit in prices or quantities. Of course, an
important reason stores deploy web technology is for inventory management and other tasks
that raise measured productivity. However, some portion is used to attract customers by
9making the entire shopping experience more pleasant or lower (transaction) cost.
On the other hand, web technology may have some impact on observable performance
measures. It is possible that a more informed consumer is ready to buy once she arrives at
the dealership and therefore takes less sales time, allowing the salesman to sell more cars per
day. A good website may increase matches between customers and the dealership or a car,
and in this way save sales staff time. However, the Internet also informs consumers of the
accurate market price of a car (Zettelmeyer, Scott Morton, and Silva-Risso 2006), and previous
work shows that people who use the Internet pay below average prices for a car. Thus, if
output of a dealership is measured as revenue or gross profit, there would potentially be two
opposing effects of attracting internet consumers: higher quantity of cars sold per salesman,
but lower gross margin per car sold, with an uncertain net effect. With our data, we are able
to measure each component of dealer performance, quantity and price, separately. We propose
the following simple model:
Consumers with mass one can purchase from either of N = 2 firms; the car gives a consumer
value V no matter where she purchases it. She must search to find a car to purchase. Search
on the Internet is less costly than search offline (s m above marginal
cost.
The consumer first contacts N dealerships with her search questions. Suppose, for this
exercise that the marginal benefit of searching both dealerships, M − m, is greater than the
cost of contacting them, N · s.
A dealership receiving a question online responds with an answer of quality r(IT, JD). The
quality of the response increases in the dealerships level of information technology (IT ), in the
dealerships level of job design and incentives (JD), and in their interaction. If all responses are
unsatisfactory (r < 0) the consumer spends S to visit a random dealership. Essentially, this
is just a cutoff below which the response is too poor to be useful and the consumer reverts to
offline shopping.
Provided at least one answer is of sufficient quality r > 0, the consumer picks the highest
response and purchases a car from that dealership.
The mass of consumers who do not have Internet access (1 − α), visit a random dealership
to purchase. Because Internet customers arrive with more information and a more specific idea
10of the car they want, we assume online customers cost the dealership c in sales time, while
offline customers cost C > c because it takes the salesman more time to match the consumer
with a car. Selling costs are below their respective margins (c < m, C < M ).
Quantity sold by a dealer is therefore a function of the local market size, α, and the answer
quality function r(IT, JD).
1
qi = αI(r(ITi , JDi ) > r(ITj , JDj )) + (1 − α)
2
Any dealership may purchase incremental IT for f (IT ), where f (·) is invertible and increas-
ing monotonically.
The dealership is endowed with JD and cannot alter it. We model heterogeneity of orga-
nizational practices across dealerships with JDj , where j indexes dealerships. We choose to
put the heterogeneity in the job design rather than the IT because of the inherent replicabil-
ity of website software. It seems more reasonable to us to assume that HR practices, social
norms, incentive schemes, and work rules vary across stores and are harder to change than the
technology.
Profits take the form,
1
πI = αI(r(ITi , JDi ) > r(ITj , JDj ))(m − c) + (1 − α) (M − C) − f (ITi )
2
This form exhibits clearly that, in this model, there is no benefit from investing in IT for a
firm that will not be the leader in providing responses to customers, and therefore capture the
Internet-using population. In this model all equilibria will be characterized by the adopting
firm being the one with higher levels of job design, JD. This brings us to our first empirically
testable implication.
Proposition 1 Firms investing in IT will be those with higher levels of Job Design
Sketch of Proof: Suppose, without loss of generality, that JDi > JDj and ITi < ITj . The
marginal cost benefit comparison for investing in IT is,
∂πi (IT, JD) −f (IT )
where ri (IT, JD) < rj (IT, JD) or ri (IT, JD) > rj (IT, JD) ,
=
∂IT α(m − c) − f (IT ) where ri (IT, JD) = rj (IT, JD)
11Since the value of capturing the Internet market is α(m − c), no firm would ever invest more
than that amount in IT . Investing weakly less than the value of the Internet market is worth
it only if the investment results in capturing said market.
For any answer quality rj and commensurate ITj , firm i is willing to invest up to the
marginal cost of capturing the Internet-user market, α(m − c), for IT 0 if the resulting ri0 from
doing so is ri0 > rj . For any ri0 < rj , ri would not be willing to invest anything, as its market
size would not grow. Given a maximum investment of f −1 (α(m − c)), this means that the
maximum answer quality that firm i would be willing to invest to attain is:
r̄i = r(f −1 (α(m − c)), JDi )
Similarly, the maximum that firm j would be willing to invest is
r̄j = r(f −1 (α(m − c)), JDj )
The increasing differences of r(·) implies:
r̄i = r(f −1 (α(m − c)), JDi ) > r̄j = r(f −1 (α(m − c)), JDj )
Thus, for any ITj and commensurate rj∗ in which firm j is willing to invest, firm is dominant
strategy is to invest in a level ITi such that the commensurate ri > rj , and firm i captures the
entire Internet-using market. Given that firm j cannot capture any Internet-users, investing in
IT is a dominated strategy, which contradicts the notion that its IT spending could be higher.
QED.
The second testable implication is that dealers who invest in IT will have a higher volume
of sales than those who do not. Firms that adopt IT capture α + (1 − α)/2. Those who don’t
capture (1 − α)/2.
The third implication, and one that this data is uniquely suitable to test, is that the average
margin of dealerships who invest in IT will be lower than the average margin for non-adopters
αm + (1 − α)M < M
In section 5 we describe the data we use to test these three implications.
125 Data
5.1 Output and operational data
Our primary dataset consists of transactions gathered from the internal IT systems of franchised
dealers. These are collected by a specialist auto data firm, hereafter MPAD. The MPAD sales
data are extremely detailed. A year’s worth of data includes all new and used car transactions
at the specified dealerships with a large number of characteristics of the car, the trade-in, and
the additional services sold by the dealer such as finance and insurance. These data allow us
to summarize the dealer‘s strategy using measures of the volume of new cars sold, the margin
on those cars, and the profits earned by the dealer on insurance and financing. We also know
the address of each buyer and the dealer, so we can create measures of market structure such
as the number of same-nameplate dealers in a geographic radius. The age of the buyer is also
known, and from this we calculate a county-level average age of all (new and used) car buyers
in that county (in our sample).
5.2 Dealer Survey
We begin with the names and address of all Connecticut, California, Pennsylvania, New York,
Oregon, and Washington franchised auto dealers. We will use all nameplates with significant
sales in the US. Practically speaking, this means all nameplates except boutique marques such
as Lamborghini. From the states’ lists we can obtain basic data about each dealership: phone,
nameplate, owner, etc.
We outsourced the actual implementation of the survey to a market research firm. Em-
ployees of the firm phoned Sales Managers of the targeted dealerships. Prior to this call, we
introduced it with a letter explaining the academic nature of the project. We stress the con-
fidentiality of the project and provide the responder with university-themed golf balls as an
indicator of both the academic orientation and our interest in speaking with them. The purpose
of the first phone call is to schedule a time to run the survey over the phone. A second item,
such as a Yale or Cal hat, is mailed to the manager upon completion of the survey. We ran a
pilot test of the survey on dealerships in Utah which is not a state we are using for the survey.
The pilot allowed us to adjust many of the incentives and logistics of the survey to improve its
design and attractiveness. A copy of the survey may be found in Appendix A.
We completed surveys for approximately 500 dealerships. The surveys were conducted
during August and September of 2008. We have merged our survey dealerships with a year of
13the MPAD sales data (the third quarter of 2007 through the second quarter of 2008). Note that
the US car market changes drastically during this time. The 2007 quarters are fairly normal,
but car sales drop dramatically in 2008.
5.3 Technology adoption: website
The website information on the survey is reported by the general manager or sales manager
of the dealership. We also collect objective data on the visible attributes of each dealership’s
website. We hired research assistants to visit the websites of all our successfully-surveyed
dealerships and rate them on what information, buttons, drop-down menus, etc, they had that
were useful to a car shopper. This work was done a few months after the survey was completed
(March-May 2009) For example, a website with an address and directions could simply contain
an address, it could have written directions from various points, or it could have a live link to
googlemaps with the address of the dealer already inserted. Likewise, a request for a quote
function could simply be instruction to telephone the dealer, a form to fill out which will
produce a follow-up call, email options, and fax options.
The physical capital inputs used in auto retailing are not standard production function
inputs like plant and equipment. The primary capital of the dealer is his inventory; a dealer
that has many varieties of model and trim on his lot will find it easier to satisfy consumer
demand. Note that dealers do own and take title to the cars on their lot, and therefore
inventory has the normal financing costs of other capital expenditure. We use the MPAD data
to construct an average inventory held by each nameplate during our time period. Then we
create a variables which is the difference between a dealer’s holdings and the average inventory
for that nameplate. We also use a measure of capital that is the dealer’s subjective valuation
of his own physical facilities relative to competitors.
5.4 Demographics
We merge data from the census into our transaction data. In particular, we gather character-
istics of the census block in which the dealer is located and the county in which the dealer is
located. We collect the 1950 population for the county as well as the population in 2000.
5.5 Summary Statistics
Summary statistics are reported in Table 1. On average, our dealerships have had the ability to
post hours and directions for 8.4 years, 7.9 for an appointment, 7.8 for a price quote, and 7 to
14search inventory online. Many dealerships report not offering consumers the ability to purchase
online; this is good in the sense that it is not possible to complete the entire process over the
web. We interpret the answer of dealerships that claim to have this capability as reflecting a
website with a streamlined process to bring consumers close to purchase completion. The mean
number of years for this capability is 3. We sum these age variables to create a “web capability”
summary measure which is reported in the table. The mean webcap is 33.9 with a standard
deviation of 14. We assume that years of capability reflects technological sophistication. The
table also shows the scores our websites obtain for both purchase ease, as evaluated by the
RAs, and information collection, also evaluated by the RAs. The latter measures how well
the website collects information about the consumer through online forms. The former reflects
whether there is an easy to use button for purchase, and functions such as payment calculator
and appointment request.
We report some operational characteristics of our dealerships. Average volume at our
dealerships is about 1600 cars over a full year, or about 130 per month. The standard deviation
is substantial relative to the mean, at 1516 cars per month. The median volume is 1200, which
reflects the existence of many small dealerships. (Recall the discussion of franchise laws above.)
Dealership average margins on a new car ranges from -1.98% to 12.49% in our sample. with a
mean of 4.37%. Used car margins range from 3 to 62% with a mean of 13%. The percentage
of cars sold that are used ranges from 0.1 to 1, with a mean of 0.66. We know how much a
dealership profits from sales of ancillary services. Finance profits as a percentage of the car’s
price average .027, which is approximately $700 on the average car. Given that the average
profit margin on a new car is about $1500, it is clear that some dealerships are earning a
substantial portion of profits through financing. The financing markup is the average number
of basis points a dealership marks up its own loans (over the rate its contract lender will offer
the customer). This ranges from zero to 1.6 percentage points, with a mean of .60. There
is heterogeneity in the amount of inventory different stores hold; the mean is -89 (recall we
normalized by average inventory of the nameplate) and the standard deviation is 171, which
reflect the large number of small dealerships.
Census data allows us to learn about the tracts and counties in which our dealerships are
located. The report percent with a college education and median household income. The
average age of car buyers in the country is calculated by averaging the ages reported in all
transactions for a particular county. We calculate the number of same-nameplate competitors
within 10 and 25 miles, the total number of auto franchises in the country, the average distance
15from a dealership to car-buyers in the area (whether or not they actually bought at that
dealership), and the growth in county population from 1950 to 2000.
Our survey yielded many interesting variables. We construct a training variable that runs
from zero to 5, according to answers to survey questions. Zero reflects no training for floor
salesmen. The variable increases in value as the dealership reports giving salesmen materials
to read, or classes to take, with higher values for internal materials and classes as opposed to
external materials and classes. The mean training level in the data is 3.5.
We can also measure how floor and internet salespeople are compensated. Compensation
may be based on (one or more of) a flat salary, a percentage of gross margin, or a performance
matrix including customer service. 83% of dealerships use percent gross as one of their metrics
but only 51% use it as the only performance metric. Most dealerships track leads fairly closely
(2.8 out of 3 for trackdetail and 3.6 out of 4 for track type). Only 36% have a centralized
group that tracks customers known as a “business development center” or bdc. This indicates
size and sophistication of the sales process. We know how the dealership assigns leads to the
floor staff. Assign equal to one indicates that the staff watches for a customer to arrive on
the lot and calls out who gets whom. Higher levels of assign indicate equal rotation (2) and
assignment based on performance (3). We track whether the dealership has an Internet Service
Manager (ism) and find that 62% of them have this position. The salesforce at our dealerships
averages 13, though the range runs from 1 to 85. Facility quality is a self-reported estimate of
the physical plant of the dealership. Ad and lead budgets vary widely; many dealerships spend
nothing, but the larger dealerships spend significant amounts.
6 Empirical Work
In many settings in Industrial Organization the researcher expects there will be heterogeneity
across firms but is unable to observe the nature of that heterogeneity when it spans both
external and internal characteristics of the firm. Table 2 shows correlations between the size
of the dealership and many policy choices. The table provides evidence of economies of scale
in auto retailing. Large dealerships, measured by volume or salesforce, invest in business
development centers, lead tracking software, CRM software, and advertising. Large dealerships
have lower gross margins per car.
16Table 1: Summary Statistics
Variable Mean Std. Dev. N
volume 1602.508 1516.922 478
dlrnewmargin 4.367 1.762 463
dlrusedmargin 13.212 4.591 462
fimargin 0.028 0.013 478
finmark 0.596 0.258 478
percnew 0.66 0.137 476
invdiff -89.205 171.1 478
pctcol 0.358 0.186 461
avgcage 45.179 2.531 478
income 56406.625 27016.308 461
comp10 0.442 0.782 430
comp25 2.395 2.791 430
countyfran 11.165 13.816 478
disttocust 51.267 17.405 426
popgrow 3.125 2.217 476
trainlvl 3.507 1.466 477
grossonly 0.517 0.5 478
ism 0.617 0.487 478
assign 1.363 0.515 477
bdc 0.365 0.482 477
trkdetail 2.847 0.449 477
tracktype 3.577 0.773 477
facqual 3.597 1.068 477
adbudget 34800.676 65009.705 444
lbudg 3134.738 6665.904 450
fte 12.79 9.303 476
webcap 33.877 13.897 478
webpurch 1.938 0.935 469
webinfo 1.763 1.106 469
17Table 2: Correlations between dealership features
Variables volume fte percnew dlrnew- invdiff trainlvl gross- bdc crm ism trk- facqual webcap web- web-
margin only detail purch info
volume 1.000
fte 0.378 1.000
(0.000)
percnew 0.085 0.046 1.000
(0.065) (0.319)
dlrnewmargin -0.122 -0.170 -0.030 1.000
(0.009) (0.000) (0.517)
invdiff 0.431 0.118 0.056 -0.115 1.000
(0.000) (0.010) (0.224) (0.013)
trainlvl 0.040 0.049 0.059 0.022 0.017 1.000
(0.388) (0.286) (0.198) (0.637) (0.705)
grossonly -0.055 -0.088 0.038 0.078 -0.067 -0.121 1.000
18
(0.228) (0.056) (0.404) (0.094) (0.144) (0.008)
bdc 0.116 0.150 0.044 -0.035 -0.011 0.097 -0.027 1.000
(0.011) (0.001) (0.343) (0.458) (0.816) (0.034) (0.556)
crm 0.105 0.100 0.085 -0.041 0.114 0.088 -0.043 0.065 1.000
(0.021) (0.030) (0.064) (0.379) (0.013) (0.054) (0.353) (0.157)
ism 0.118 0.105 0.033 -0.132 0.026 0.134 -0.004 0.066 0.117 1.000
(0.010) (0.021) (0.469) (0.004) (0.573) (0.003) (0.935) (0.149) (0.010)
trkdetail 0.093 0.101 0.083 -0.030 0.047 0.115 -0.067 0.113 0.347 -0.018 1.000
(0.041) (0.028) (0.070) (0.521) (0.305) (0.012) (0.142) (0.014) (0.000) (0.697)
facqual 0.112 0.047 0.079 -0.004 0.089 0.082 -0.006 0.147 0.040 0.031 0.090 1.000
(0.015) (0.306) (0.087) (0.924) (0.053) (0.072) (0.892) (0.001) (0.383) (0.495) (0.048)
webcap 0.106 0.086 0.034 -0.078 0.088 0.103 -0.070 0.029 0.163 0.110 0.037 0.084 1.000
(0.021) (0.062) (0.457) (0.095) (0.055) (0.025) (0.124) (0.522) (0.000) (0.016) (0.425) (0.066)
webpurch 0.208 0.110 0.003 -0.109 0.082 -0.053 0.031 0.124 0.075 0.109 0.027 0.050 0.039 1.000
(0.000) (0.017) (0.948) (0.020) (0.076) (0.255) (0.500) (0.007) (0.104) (0.018) (0.561) (0.285) (0.405)
webinfo 0.087 0.100 -0.023 0.025 -0.051 -0.031 0.042 -0.001 0.036 -0.031 0.140 0.091 -0.032 -0.051 1.000
(0.060) (0.031) (0.622) (0.597) (0.273) (0.505) (0.368) (0.977) (0.440) (0.510) (0.002) (0.049) (0.483) (0.267)Consistent with Ichniowski, Shaw, and Prennushi (1997), Milgrom and Roberts (1980)
etc. we see correlation of “good”, or what we might call consumer-oriented, organizational
and human resource practices within a dealership. For example, a high score on our training
variable is correlated with performance-based compensation, keeping track of leads, and having
an internet sales manager.
One concern we want to address is the possibility that many of the dealership’s policy
choices are driven by the nameplate, both because of constraints from the manufacturer and
because of the positioning of the brand in product and demographic space. One might wonder
if these correlations remain once nameplate fixed effects are included. Conditional correlations,
in the form of regressions of volume on these various characteristics controlling for nameplate
fixed effects, demonstrate similar patterns compared to the unconditional correlations. We do
not report these regressions as they are numerous and do not materially add to the story. There
are two important differences to report. Training and performance compensation are no longer
positively correlated with dealership size once nameplate is conditioned on.
We run some descriptive regressions explaining the pattern in the new car margin. These are
also unreported for space considerations and because they can be easily summarized. In these
regressions we put a dealer’s new car margin on the left hand side, control for nameplate fixed
effects and either include a competition measure or a county fixed effect. The results generally
show that the conditional correlation between the margin on new cars and a dealer’s volume
is negative. Additionally, the correlation between new car margin and a compensation policy
based on margin is positive. This latter result is reassuring to see because the information on
the compensation policy comes from our survey, while the new car margin data come from
our transaction-level dataset. The positive relationship gives us additional confidence in our
survey.
We would like to understand whether these policy choices appear in clusters among our
dealerships. Because we have product market strategy variables, variables measuring internal
policies, and variables measuring technology adoption, we undertake a factor analysis to orga-
nize the data. Table 3 reports the results of a principle components factor analysis, retaining
the 5 factors with the largest eigenvalues. What we see is that the most distinguishable strat-
egy is “high volume-low margin.” These dealers are very large and have low profit margins on
new cars. They sell new cars disproportionately, carry high inventory, track leads vigorously,
and have good web technology. These competitive dealerships face many competitors and are
located close to potential customers.
19Table 3: Correlations between factors and variables
Variable Competitive Sheltered Finance/used Niche Stereotypical
volume 0.6314 0.3212 0.0803 -0.1862 0.3573
fte 0.517 0.3112 -0.0545 -0.0181 0.3678
invdiff 0.5797 0.0991 0.0513 -0.3136 0.0866
dlrnewmargin -0.3311 0.039 0.1339 0.429 0.3553
dlrusedmargin -0.3978 0.1148 0.0573 0.1765 0.6343
fimargin 0.1557 0.367 0.7037 0.1623 -0.021
finmark 0.088 -0.2403 0.6556 0.1483 -0.2439
percnew 0.3008 -0.1513 -0.3845 0.1312 -0.1131
comp10 0.3562 -0.3869 0.2623 0.0337 0.3491
disttocust -0.4632 0.7435 0.0107 -0.1475 -0.1332
avgdisttocomp -0.4525 0.7499 0.0063 -0.2068 -0.1077
franppop2000 -0.4846 0.1531 0.2342 0.0055 0.1298
assign -0.1151 0.0948 -0.3214 -0.129 0.0466
grossonly -0.0817 -0.0794 0.2306 -0.0091 -0.1322
trainlvl 0.1106 0.2493 -0.1541 0.3749 -0.0582
trkdetail 0.3378 0.2821 -0.0474 0.543 -0.2422
tracktype 0.3687 0.2253 -0.1238 0.4215 -0.2457
bdc 0.2908 0.1802 -0.1234 0.1818 0.1326
ism 0.3211 0.1635 0.0121 -0.2181 -0.3376
facqual 0.1949 0.3137 -0.3012 -0.0206 0.1297
webcap 0.2399 0.0644 -0.3 -0.0241 0.0391
webpurch 0.2798 0.1573 0.1924 -0.2988 0.1428
webinfo 0.0183 0.1928 -0.052 0.3851 0.1061
avgdage -0.4047 -0.2773 -0.3991 0.0506 0.1047
20The strategy listed next we call “sheltered” for lack of a better term because these dealers
face low intrabrand competition, and are further from customers. They are also fairly large but
their margins on new cars are higher and they earn money from financing and insurance. The
internal policies of these dealerships show that they invest in training, lead tracking, physical
facilities, and websites. We infer that these investments allow these dealerships to provide
high-quality service to customers.
The third strategy is one based on profits from financing, profits from interest rate markups,
and profits from sales of used cars. These dealerships have incentive pay based on gross margins,
as one might expect. They also have poor internal policies for lead assignment, training, lead
tracking, and poor facilities.
The fourth type of strategy we call a niche strategy. Dealers in this group have high margins
and low volume and low levels of inventory. However, they make some money from financing
and they invest in their dealerships with high training levels and tracking of customer leads.
They appear not to systematically invest in other fixed attributes (website, physical facilities)
perhaps due to lack of economies of scale.
The last strategy we describe is a stereotypical one of high margins and low service. These
dealerships are relatively large and have high margins on new cars and very high margins
on used cars despite intrabrand competition. They do not track leads or have an internet
department. They have average websites and physical facilities. Customer age is positively
correlated with this factor, and negatively correlated with the first three factors. We suspect
that the correlation with age arises because older consumers tend not to use the Internet as
much and so do not engage in as much price search. Therefore, these high margin dealerships
will have a disproportionate share of “uninformed” consumers among their customers.
Table 4 lists the correlations between nameplates and our five strategies for the interest of
readers who might also be car buyers. Clearly, the type of cars being sold will determine cus-
tomer demographics to some extent, as will franchise policies concerning geographic territories,
IT use, promotions, etc. However, one can see that most of these nameplates exhibit multiple
strategies within the dataset because they are either correlated with more than one franchise
strategy, or are uncorrelated with most of them.
6.1 Instruments
We need instruments that impact a dealership’s use of IT but yet are not part of the cluster of
choices of dealership management. The characteristics of the local population and competitive
21Table 4: Correlations between nameplates and our five strategies
Competitive Sheltered Finance/used Niche Stereotypical
Acura ++ – - –
Audi -
BMW ++ -
Buick –
Cadillac –
Chevrolet - + ++
Chrysler ++ –
Dodge ++ – –
Ford – ++ ++
GMC –
Honda ++ + - ++
Hyundai ++
Infiniti -
Jaguar –
Jeep ++ –
Kia + ++
Lincoln – ++
Mercury – +
Mitsubishi – –
Nissan ++ ++ +
Pontiac –
Porsche –
Saturn – ++
Scion ++ ++ ++
Toyota ++ ++
VW –
Volvo – -
LandRover –
Mercedes ++
environment determine many aspects of the dealership including IT but clearly are not deter-
mined by dealer policies. Of course, dealer location will be optimally chosen at the time the
dealership is launched. However, many dealerships were located decades ago when demograph-
ics were quite different. Additionally, as one can see from Table 4, all nameplates show variety
in the strategies they pursue in terms of pricing and selling cost. Lastly, these dealerships were
almost all established before the internet became a major issue in auto retailing.
We include percent college and income in the census block in our group of instruments.
Our instruments at the county level are average age of the car buyers in the county, population
growth since the 1950’s, and total number of auto franchises in the county. The former drives
web adoption, the latter are determinants of competition and scale. Other county level variables
we use are from the survey of business use of IT detailed in (Forman, Goldfarb, and Greenstein
2009). We use two of their measures: participation, which measures fairly basic internet use
by businesses in the county, and enhancement, which indicates more sophisticated use of the
22internet by businesses in the country. The dealerships in our sample are in counties that have a
mean of .83 for participation and .11 for enhancement. We expect that building and maintaining
a website in geographic areas with deep and well-developed labor markets of IT professionals
and service organizations is lower cost. Thus we expect higher measures of enhancement to
proxy for lower costs of creating and maintaining a website.
How much competition a dealership faces is also pre-determined relative to web technology
adoption and transaction volume, particularly for the US nameplates. We include the number
of competitors of the same nameplate within 10 miles, whether the name plate is of US origin,
and interactions between number of competitors and population growth and US nameplate
and population growth. The component of market structure that is driven by old franchise
territories (proxied by population growth) is arguably exogenous to the dealer‘s behavior and
policy choices. Additionally, for each dealer, we also calculate the average distance to (all) auto
consumers in our dataset and use this an an instrument. These variables vary at the dealership
level.
We report first stage regressions in Table 5. The explanatory power of our instruments is
fairly low, but a number of our instruments attain p-values of below .1. The three columns of
Table 6 show an ordered probit regression for the web purchase measure, a poisson regression
for web capability, and a probit for Internet Sales Manager.
23Table 5: First stage of Instrumental variables regressions
Ordered Probit Poisson Probit
(1) (2) (3)
VARIABLES webpurch webcap ism
pctcol -0.534 0.171** 0.336
(0.480) (0.073) (0.574)
income 5.30E-07 5.00E-07 6.67e-07
(0.000) (0.000) (3.98e-06)
comp10 0.361** -0.0375* -0.103
(0.147) (0.022) (0.167)
usname 0.0878 -0.142** -0.00167
(0.416) (0.066) (0.491)
enhancement 1.008 0.238 0.803
(1.695) (0.263) (2.008)
popgrow 0.0395 0.0113* 0.0262
(0.045) (0.007) (0.0581)
avgcage 0.0348 0.0129*** -0.0461
(0.028) (0.004) (0.0333)
growcomp10 -0.0587 0.00564 -0.0174
(0.049) (0.007) (0.0571)
growcomp25 0.00786 -0.00290** 0.000816
(0.008) (0.001) (0.00933)
growus -0.0142 -0.0114 0.0392
(0.059) (0.009) (0.0719)
pop20 8.65E-08 1.56E-10 -4.86e-07**
(0.000) (0.000) (2.41e-07)
participation 0.372 -0.131 0.492
(0.890) (0.137) (1.028)
distus -0.00455 0.00260** -0.00681
(0.008) (0.001) (0.00949)
disttocust 0.00749 -0.00437*** -0.00131
(0.007) (0.001) (0.00826)
countyfran -0.00267 0.00215** 0.0151**
(0.006) (0.001) (0.00747)
Constant -0.245 1.939
Observations 365 370 370
Pseudo R-sq .0181 .0219 .0553
Standard errors in parentheses - Second Stage reported in Table 6
*** pTable 6: instrumental variables regressions of dealer volume and margin on measures of IT
(1) (2) (3) (4) (5) (6) (7) (8) (9)
VARIABLES volume ln(volume) margin volume ln(volume) margin volume ln(volume) margin
webpurch 1346*** 0.723*** -0.357
(421.8) (0.229) (0.416)
webcap -2.289 -0.00310 -0.116***
(23.57) (0.0128) (0.0427)
ism 2152*** 1.148*** -2.118***
(632.2) (0.338) (0.760)
Constant -968.0 5.749*** 5.018*** 1718** 7.257*** 8.294*** 279.1 6.425*** 5.657***
(822.4) (0.447) (0.811) (809.2) (0.440) (1.461) (408.0) (0.218) (0.487)
25
Observations 365 365 356 370 370 361 370 370 361
Standard errors in parentheses
First Stage reported in Table 5
*** p6.2 Effect of IT
We carry out simple correlations and instrumental variables regressions corresponding to our
three hypotheses in this section. We analyze three measures of dealer IT policies: two measures
of website capability and one measure of organization and website capability, the existence of
the ISM. The first web measure was created by examination of the website attributes by RAs,
the second by the survey of Sales Managers. We ask if either measure is significantly predictive
of volume or new car margins at the dealership level. The IV regression results are reported in
Table 6.
The first columns show the results from an IV regression of website purchase capability. The
estimated coefficient on volume, whether in logs or levels, is positive and statistically significant.
The coefficient estimate of .72 implies that a one-standard deviation in crease in web purchase
rating results in an increase in quantity of cars sold by 60%. In levels, the estimated coefficient
of 1300 implies a one standard deviation increase in the web purchase measure increases cars
sold by about 1200 per year (mean 1600). The IV regression of web capability on quantity of
cars sold shows no significant effect at all. Turning to the instrumented regressions of new car
margins, we find the opposite. The web purchase measure has no impact on margins. However,
the web capability measure has an estimated coefficient of -.12. If web capability increased one
standard deviation (14), then new car margins would be predicted to decline by 1.5 percentage
points. This magnitude seems reasonable given that new car margins range from -2 to +12 in
the data, with a mean of 4.4 and a standard deviation of 1.8. find that both measures predict
lower margins.
We return to the question of whether a dealership that adopts both the website and the
accompanying organizational change has a stronger impact on either volume or margin. Our
measure of both together is the dummy variable “Internet Sales Manager” or ISM. In the
remaining columns of table 6, we use the same instruments to predict the choice of having an
Internet Sales Manager. We find that the relationship between ISM and volume is strongly
positive and significant, with an estimated coefficient of 1.15. This coefficient suggests that
adopting an ISM increases sales by 15%, which is a smaller effect than the one we found using
instrumented web purchase ratings. The estimated coefficient in the levels regression (2100) is
more similar to the previous result with web purchase. The final column shows the estimated
impact on dealer new margin from adopting an ISM. It is negative and significant at -2.11,
which is similar in size to the coefficient on web capability.
Our final hypothesis is that the dealerships that choose to adopt IT will have higher levels
26You can also read
