Feature Overload - INSEADflora.insead.edu/fichiersti_wp/inseadwp2011/2011-66.pdf · Feature overload refers to the phenomenon wherein ... olution, image stabilization, lux ... recent

Feature Overload

_______________

Kaifu ZHANG V. PADMANABHAN 2011/66/MKT

Feature Overload

Kaifu Zhang*

V. Padmanabhan**

May 31, 2011

Preliminary draft. Comments are welcome

* PhD Candidate in Marketing at INSEAD, Boulevard de Constance, 77305 Fontainebleau,

France. Email: [email protected] ** The John H. Loudon Chaired Professor of International Management and Professor of Marketing

at INSEAD, 1 Ayer Rajah Avenue, 138676 Singapore, Singapore. Email: [email protected]

A Working Paper is the author’s intellectual property. It is intended as a means to promote research tointerested readers. Its content should not be copied or hosted on any server without written permissionfrom [email protected] Click here to access the INSEAD Working Paper collection

Feature Overload

Abstract

Feature overload refers to the phenomenon wherein consumers purchase feature rich products but

subsequently don’t use all the features. We try to understand why this occurs as an equilibrium

outcome. We focus on two aspects of consumer preference: the uncertainty about feature need

and the complexity disutility from too many features. We show that consumer uncertainty creates

an option value even if the feature is unused ex-post. In a monopoly setting, the firm offers the

feature rich product both to enhance valuation and for pricing reasons. In the later case, feature

rich product may be offered even if the overall complexity cost outweighs the option values of

additional features. In the competitive case, feature rich product may be offered in two types of

equilibria: firms may compete with each other on the number of features, leading to a prisoner’s

dilemma situation where both firms offer the feature rich product. Firms may also differentiate on

the number-of-feature dimension, therefore engaging in ‘uncertainty based segmentation’. In the

later case, competing products use ‘functionality’ and ‘simplicity’ as their respective value propo-

sitions, and joint profit is maximized. Interestingly, higher complexity disutility lowers profit in the

monopoly case but may raise equilibrium profits in the duopoly case. To provide support on our

utility function assumptions, we develop a preference measurement methodology and empirically

assess feature need uncertainty in a field study. The empirical results point to both the existence of

and the heterogeneity in feature need uncertainty.

1

1 Introduction.

One of the authors had to purchase a camcorder at short notice because he realized the old one

was broken and his daughter’s concert was just a few days away. The bewildering array of features

within and across brands made the choice process quite difficult. How should one trade-off res-

olution, image stabilization, lux ratings, video capture formats, image sensors, interface options?

Not being a techie and not knowing when he might need a particular feature, he proceeded to buy

a Canon Legria with everything bundled in. However, it was not long before he found himself

deeply lost in the plethora of options and an encyclopedia-like user mannual.

Unfortunately, the author is not alone in his frustration with complex gadgets nowadays. A

recent survey by a California-based research company reveals that people over 30 only use 12%

of the features in their mobile phones. Moreover, one third of these respondents expressed ’deep

frustration’ over their handsets. This ’feature overload’ phenomenon 1 has become an important

source of consumer dissatisfaction and complaints.2 Interestingly, anecdotal evidence suggest that

many companies are aware of the feature overload problem as well as the possibility to profit from

this problem. Sony, for example, offered to remove the pre-installed softwares in their laptops in

2008. Being aware of consumer complaints over unneccessary softwares, they planned to charge

a price premium for the computers loaded with fewer softwares. Some other companies have hit

success with simple and usable products. One of the most successful products in the camcoder

category is the Flip with a market share of 13% that retails for $130. It just records videos and has

no menus, no settings, no optical view-finder, no video light, no special effects, no high definition,

no lens cap, no optical zoom and no memory card (New York Times, December 21, 2008).

The feature overload phenomenon refers to bundling all the features within a single product.

1Often referred to as feature creep in the popular press (Financial Times - November 12, 2005, Business Week -April 13, 2006 and New York Times - July 16, 2009).

2In this study, we formally define feature overload as offering a product with several features even if it is commonknowledge that none of the consumers will use all of these features post purchase.

2

Thompson, Hamilton and Rust (2005) in a recent paper provide a very insightful perspective on this

phenomenon. They show through a series of experiments that consumer evaluations and choices

are systematically influenced by the feature attributes of a product. In short, ex-ante consumers

prefer feature rich product but ex-post prefer the less-feature product. In this paper, we attempt to

build a consumer utility function that predicts this preference reversal phenomenon, and study firm

decision making in an equilibrium framework.

A careful examination of the academic and business literature on feature overload suggests

that consumer uncertainty and complexity disutility are key drivers of the phenomenon Paddy:

what are the citations? Because I thought the two aspects in consumer utility are assumptions

made by ourselves.. Uncertainty in this context refers to the ability to forecast likelihood of usage

contexts but could as well be about credibility of the claims of value added benefits of features.

Complexity disutility in this context refers to the cost on the consumer side as firms include more

features in the product. We build a model that incorporates these two elements to answer questions

that have not been studied earlier. Specifically, we seek to answer the following questions

• What are the implications of consumer uncertainty and heterogeneity for firm’s decisions on

feature provision in their product? How does this interact with the firm’s ability to market

an assortment of products?

• What are the equilibrium implications of the strategic interaction of firms in the context

of consumer uncertainty and heterogeneity? Are the drivers behind the ‘feature overload’

phenomenon different in the monopoly case and under compeition?

• What are the consequences of endowing firms with the ability to offer optional feature up-

grades on equilibrium outcomes and welfare?

Our analysis reveals several key insights into the feature overload phenomenon. In a mo-

nopolistic setting, offering a feature rich product represents a basic trade-off between option value

3

and complexity cost. Interestingly, the firm may have the incentive to offer the feature-rich prod-

uct even when the complexity cost outweighs the average option value provided by the additional

feature. This points to the role of the feature-rich product as a device to reduce consumer hetero-

geneity, since it provides one-model-fits-all insurance against all possible consumption state.

In the competitive setting, we find that offering the feature rich product can be either a

prisoner’s dilemma outcome or an efficient outcome that maximizes joint profit. In the former

case, firms try to outcompete each other by loading more features into their product. This leads to

a situation where both firms offer feature rich product, and become worse off due to the heighten

competition. When complexity cost is sufficiently large, however, firms can differentiate by of-

fering the simple product and the feature rich product respectively. Simplicity and functionality

become their respective value propositions, which lead to more focused positioning and less com-

petition. These findings relate to anecdotal evidences. Interestingly, we observe that the firms’

profit can both raise when complexity cost becomes higher.

The rest of the paper is organized as follows. We review the related literature in Section 3.

The model setup is presented in Section 4. Section 4 and 5 present our key results and extensions.

In Section 6, we propose a measurement technique of consumer uncertainty and apply it in a field

study, which intend to illustrate the key factor underpins our analytical model. Section 7 concludes.

2 Related Research

This paper is related to several streams of literature. The effect of product features on individual

choices has been extensively studied in the consumer behavior literature. A robust finding was

that more features are not always better. Simonson, Carmon, and O’Curry (1994) illustrated how

additional features make consumers less likely to choose a product, even if these features clearly

does not diminish the value of the brand. Mukherjee and Hoyer (2001) articulated the link be-

tween additional features and product complexity. They demonstrated that additional features may

4

decrease product evaluation for high complexity products. Moreover, the literature suggests that

when choosing between feature rich product and simple product, consumers exhibit a preference

reversal tendency. Thompson, Hamilton, and Rust (2005) provided a systematic experimental ex-

amination of how consumers’ product evaluations and choices are influenced by the number of

product features. Their study revealed a preference reversal phenomenon: Before actual usage,

consumers prefer the more feature products. Once having used the products, however, they tend to

prefer the less-feature products. Furthermore, when the consumers are asked to rate the products

on capability and usability, they give inconsistent ratings before and after usage. Meyer, Zhao,

and Han (2008) find evidence of a valuation-usage disparity for product capabilities. Consumers

have high willingness to pay for products with expanded set of features, but do not use these fea-

tures ex-post. The above studies provided the behavior basis for our consumer utility function. In

the next section, we will introduce the consumer utility function in our model. Our formulation

of consumer utility function will predict the preference reversal phenomenon as in (Meyer et al.

2008, Thompson et al. 2005).

Our study does not investigate the role of ’biases’ in explaining the feature overload prob-

lem. We show that feature overload emerges without the introduction of any behavior bias. Behav-

ior ‘biases’ might further exacerbate the problem. For example, Shin and Ariely (2004) demon-

strates that people are willing to make significant investment to ’keep options open’, even when the

options themselves seem to be of little interest. Della Vigna and Malmendier (2006) demonstrates

overconfidence about personal efficiency and self control may drive consumer purchase gym plans

which they subsequently under utilize. Meyer et al. (2008) explain the disparity between valua-

tion and usage with an intertemporal choice model, in which consumers are subject to hyperbolic

discounting.

Our study is also related to the economics and marketing literature on preference uncer-

tainty. Preference uncertainty arises when consumers have state dependent utility, and are uncer-

5

tain about the states. It is a driver to various consumer behavior, such as brand loyalty (Villas-Boas

2004) and the choice of service contract and multi-part tariff. (Lambrecht, Seim, and Skiera 2007,

Narayanan, Chintagunta, and Miravete 2007). The literature on preference uncertainty suggests

a link between state dependent preference and the need to maintain consumption flexibility. For

example, Hauser and Wernerfelt (1990) has shown that a consumer’s optimal consideration set

usually contains more than one item when preference uncertainty is important. Guo (2006) find

that consumers may purchase multiple products in face of state dependent utility. As a result, firms

cease to compete with other, and larger product differentiation can lead to smaller profit. In similar

veins, we argue that consumers may have state dependent utility for the features, and their prefer-

ence for the feature rich product is driven by the need to maintain flexibility. In this respect, our

model shares the logic with (Guo 2006), while the ’multiple buying’ behavior is on the product

feature level. The main difference between our study and (Guo 2006) is our assumption of a ’base

product’, which makes it too costly for the consumers to buy multiple base products with different

features 3. In the service literature, a few mechanisms have been proposed to leverage consumer

preference uncertainty for greater profit, such as advance selling (Xie and Shugan 2001), service

upgrade (Biyalogorsky, Gerstner, Weiss, and Xie 2005) and allowing service cancellation (Xie and

Gerstner 2007). Advanced selling refers to selling the service to consumers before the resolution of

their preference uncertainty. Service upgrade allows the consumers to upgrade their pre-purchased

services when they observed the state of nature. Service cancellation gives the consumers the

option to cancel their service, and the firm is able to profit by both charging cancellation fees and

resell the freed capacity. All these instruments depend on the difference between ex-ante consumer

valuation and ex-post consumer valuation, and in this light, share the same insight as our work.

Finally, our study is related to the economic literatures of bundling and product line compe-

tition. Bundling (Bakos and Brynjolfsson 1999, Matutes and Regibeau 1992, McAfee, McMillan,

3For example, consider a GPS navigator producer who is offering a altimeter and a floating case as features. If theprice of the GPS is high, the consumers will never buy a GPS with a altimeter and another GPS with a floating case.

6

and Whinston 1989, Nalebuff 2000, Stremersch and Tellis 2002) is defined as ’the sale of two

or more separate products in one package’ (Stremersch and Tellis 2002). ’Separate’ products are

products for which separate markets exist. In the bundling literature, the firm faces the choice

between selling separate products in different markets, or selling the bundle. In our model, fea-

tures are not sold as independent products. 4 The absence of multiple markets, and the presence

of a base product make the feature overload problem conceptually different from a bundling story.

However, as we will detail in the result section, some of our results share the same intuitions behind

the bundling phenomenon. In considering multiple product competition, our model is related to

the product line rivalry literature (Brander and Eaton 1984, Gilbert and Matutes 1993, Klemperer

1992, Verboven 1999). These authors found that firms may either offer interlaced product lines or

identical product lines, depending on the degree of brand-level differentiation.

The rest of the paper is organized as follows. The next section develops the basic model.

Section 4 analyzes firms’ product decisions in both the monopoly scenario and under competition.

Section 5 presents an extension of the model in which we allow the firms to sell optional feature

upgrades after selling the base product. In section 6, we introduce the measurement technique and

access consumer uncertainty in a field study.

3 The Model

3.1 Consumers

We consider consumers who derive consumption values from both the base product (e.g., a GPS

navigator) and the product features (e.g., a built-in altimeter or a floating case). The consumers

have state independent preference for the base product and state dependent preference for the

features. When they purchase the feature rich product, they incur a state independent complexity

4Consider our GPS example again. While this looks similar to bundling the GPS navigator with the altimeter, wefocus on a case where the GPS producer doesn’t become sellers of both GPS and standalone altimeter. Each consumerneed only one unit of the base product, and additional features only influence their valuations of the base products.

7

cost.

Let’s assume that the firm offers a base product, which could include two additional fea-

tures, a and b. A consumer’s valuation of each feature is state dependent and cannot be foreseen at

the time of purchase. There are two states of nature, Ω = {A,B}. Feature a will be useful only in

state A, and feature b only in state B. In our GPS example, the base product is the GPS navigator,

and the features a and b correspond to the altimeter and the floating case. The consumer need for

the features depend on the type of the activity: the altimeter is useful only if the consumer uses

the GPS for hiking, while the floating case is useful if she uses the GPS for surfing. Consider a

consumer who is still deciding where she will live for the next year: a seaside city in southern

France or the mountainous region of Switzerland? Depending on the location, she will engage in

one activity or the other (hiking or surfing), and will therefore need one feature or the other.

The above example illustrates a case where feature need uncertainty arise from a con-

sumer’s uncertainty about the consumption context. The uncertainty can also arise from the lack

of technical knowledge. For example, a consumer may consider a built-in camera in a laptop com-

puter useful. However, will the built-in camera deliver acceptable video quality? The consumer

may be uncertain about this if she is not familiar with computer cameras in general. These sources

of uncertainty are particularly important in fast developing markets of technical products, such as

the consumer electronics market.

We consider three possible product offerings: the product with only feature a, the product

with only feature b, and the product with both features. For simplicity, we denote a product by the

features it has: product a (b) refers to the product with only the a (b) feature,and product ab refers

to the product with both features. We denote a consumer’s state dependent valuation of product i

under state ω as v(i,ω). For example:

v(b,ω) =

{v ω = Bv ω = A

8

For product a, the consumer valuation is v only if the state of nature is A. This formu-

lation of state-dependent preference is widely used in the theoretical works on preference uncer-

tainty(Guo 2006, Villas-Boas 2004, Xie and Shugan 2001). For product ab, the consumer has to

bear a state independent complexity cost. However, he has the flexibility to pick which feature to

use after the state of the world is observed. Formally,

v(ab,ω) = max{v(a,ω),v(b,ω)}− γ

Given the above notations, v is the state independent utility from the base product. The parameter γ

stands for the complexity disutility associated with the feature rich product. Complexity decreases

consumer valuation of the product, either for engineering reasons (the basic functionality of the

product degrades as more features are included) or usability reasons (product becomes more dif-

ficult to learn and operate). Finally, such cost can be a result of consumer perception (Chernev

2007): where a ‘generalist’ product is perceived as inferior on each dimension to specialist prod-

ucts even if the features offer the exact same functionality. We assume that the consumers are

aware of the cost associated with feature-rich product before actual usage, following the empirical

findings by Thompson et al. (2005).

We capture consumer uncertain by a belief parameter, θ . θ is the consumer’s belief that

the state of nature is B. If θ is close to 0 or 1, the consumer is relatively certain about his feature

need. If θ is close to 0.5, the consumer is highly uncertain 5.

We assume that the consumers are heterogeneous with respect to their uncertainty. A ded-

icated surfer is certain that he is not going to use the altimeter in the GPS, while a person who is

interested in both surfing and hiking is uncertain about his feature need if he is still deciding which

city to stay and which activity to learn. Without loss of generality, we assume θ is uniformly dis-

tributed on [θmin,θmax] with density 1. Since we do not restrict θmin +θmax = 1, this setup allows

5Alternatively, θ could be interpreted as the expected usage frequency of this feature. Under this interpretation,the consumer indeed need both features in the long run, although he will not use both features simultaneously in anytime period. See Guo (2006) for further explaination on this interpretation.

9

us to capture the asymmetries in feature need. We call θ the type of consumers.

Will consumers prefer the feature rich product or the simple product? Net of price, the

ex-ante and ex-post preference can be illustrated by the following figure:

A

Product ab

B

v

v v

Product b

A B

* (1 ) *v v

v v

Ex-ante valuation

Ex-post valuation

Product a

A B

(1 ) * * vv

v v

Figure 1: Ex-ante and Ex-post Product Valuations

Figure 1 illustrates the valuations for the possible products. For a sufficiently uncertain

consumer, the ex-ante valuation of the ab product exceeds that of either the b or the a product.

However, after the resolution of uncertainty, the consumer strictly prefers one of the simple product

to the ab product. This pattern of individual preference is consistent with the preference reversal

phenomenon documented in previous behavior literatureThompson et al. (2005).

In the case of firm competition, we further assume that consumers have heterogeneous

brand preference. This captures the product differentiation due to the factors other than product

features. Firm 1 is located at x = 0 and firm 2 is at x = 1. Consumers are uniformly distribution on

[0,1] with ‘transportation cost’ t. Overall, the distribution of consumers is uniform with f (x,θ) =

1,(x,θ) ∈ [0,1]× [θmin,θmax]. For ease of exposition, we choose to present the results when θ is

distributed from [θmin,1]. This special case is able to provide all the main insights.

Paddy: can you do some word-smithing for the following paragraph - I imagine these

10

are some assumptions the reviewers may criticize. It might be better for us to be preemptive.

A few important caveats apply to our model of consumer utility function. First, we do not assume

heterogeneity in complexity cost. One important feature of many consumer electronic markets is

that consumers have largely heterogeneous technical expertise. The cost of complexity (i.e., more

product features) is higher for novice consumers and lower for experts. Our initial analysis of

this case indicates that heterogeneity in complexity cost is a moderator of our results. Second, we

assume a Bernoulli distribution for feature need and a uniform distribution for consumer uncer-

tainty. In reality, consumers may be certain about their need for one feature yet uncertain about

another. Moreover, they may need multiple features simultaneously or none at all. In addition, the

distribution of consumer belief may be non-uniform. These are important possibilities we do not

consider in our simplified model. Our analysis leads us to believe that our main results will remain

valid under two conditions: the consumers’ need for feature are state-dependent, and consumers

are heterogeneous with respect to their uncertainty about state. Intuitively, these condition means

that the feature rich product provides ‘all-wheather’ insurance against various consumption needs,

but the value of such insurance is different for each consumer. It implies that the ex-ante valuation

of the feature rich product should be close to its ex-post valuation, while the ex-ante and ex-post

valuation of the simple product are more likely to be different. Empirical findings from previous

literature do seem to support this implication (See Table 1 of (Thompson et al. 2005)).

3.2 Firms

The firms have constant marginal cost c for product a and b, and c for product ab. We assume that

c is sufficiently high compared to γ , and the difference between c and c is small. This is true when

the base product is relatively costly. Consider the example of the GPS navigator, this assumption

says that the production cost of the GPS device is much higher than the cost of adding the altimeter

or the floating case.

11

In the monopoly case, we consider a two stage game. In the first stage, the firm chooses its

product offering and set prices. In the second stage, the consumers purchase their most preferred

product. The expected surplus from product i for consumer type θ is:

Uθ (i) = (1−θ)∗ v(i,A)+θ ∗ v(i,B)− pi

Here, pi is the price of product i. We assume consumers are risk neutral expected utility

maximizers. When firms offer both ab and b products, for example, a consumer will prefer ab to b

iff

Uθ (ab) = v− γ − pab >Uθ (b) = (1−θ)∗ v+θ ∗ v− pb

From the locations of the marginal consumers, we can derive the demand for each product,

and obtain the firm’s profit function.

In the competitive case, we consider a three stage game. In the first stage, the firms decide

on their product offerings. In the second stage, the firms set their prices. In the third stage, con-

sumers choose which product to purchase. In this case, a consumer preference depends on both

the product features and his horizontal location. For example, a consumer located at (x,θ) derives

a utility from consumption of product b from firm 1:

Ux,θ (b) = (1−θ)∗ v+θ ∗ v− t ∗ x− p1b

Similarly, he derives the following utility from consuming product ab by firm 2:

Ux,θ (ab) = v− γ − t ∗ (1− x)− p2ab

The marginal consumers who are just indifferent between the product offerings can be derived

accordingly. The marginal consumer is defined by an implicit function of x and θ . For each product

offering, we obtain a large number of possible demand schedules depending on the parameters. The

derivations are detailed in the appendix.

12

Paddy: can you do some word-smithing for the following paragraph - I want to point

out that mathematically the model is a product differentiation model from the beginning. I’m

afraid that the reviewers will say this is a new story based on an old (2-D product differentia-

tion) model, and we just label the dimensions differently. Our model can be considered a prod-

uct differentiation model with two dimensions of consumers heterogeneity. Different from a simple

model (e.g., Hotelling) of 2-D product differentiation, the differentiation between the feature rich

product and the simple product can be either horizontal or vertical. The nature (i.e., vertical vs

horizontal) and degree of differentiation is driven by both consumer uncertainty and complexity

cost. Conceptually, we believe these two factors captures the specificities of the number-of-feature

decision. Technically, these factors create complexities beyond the simple model of 2-D product

differentiation. We provide detailed explaination in the analysis section.

4 Analysis

Table 1 represents our roadmap of analysis. We vary our model assumptions along two dimensions:

first, firms can offer either a single product or multiple products. Second, firms may be either

monopoly or in competition.

Economies of Scale?

Single-Product Multi-Product

Competition?Monopoly Section 4.1 Section 4.3

Duopoly Section 4.2 Section 4.4

Table 1: Modeling Roadmap

The single-product versus multi-product distinction captures an important aspect of firms’

cost structure. When firms face strong economies of scale in production, producing several dif-

13

ferent models will dramatically increase the average production cost. When economies of scale

is weak, firms may produce multiple products to satisfy diverse consumer tastes. Although we do

not model economies of scale explicitly (i.e., model the average cost as a function of quantity pro-

duced), considering these polar cases sheds light on the cost side of the feature overload decisions,

therefore complementing our emphasis on the demand side explanation for this phenomenon. The

monopoly versus duopoly distinction captures the level of competition in the marketplace. Our

results indicate that competition dramatically changes the firms’ incentives of offering feature rich

products.

4.1 Single-Product Monopoly

4.1.1 Equilibrium Results

A single-product monopoly faces a choice between offering the simple product a, b or the feature

rich product ab. The firm always prices the product to extract as much consumer surplus as possi-

ble. When consumers have heterogeneous beliefs θ about the state of nature, their valuation of the

product features are also heterogeneous. Ex-ante, both features may have option values even if a

consumer knows he will need only one feature post purchase. These intuitions translate into two

drivers behind the firm’s incentive to offer the feature rich product:

• Option Value The firm offers the feature rich product because all product features provide

consumers option values. Higher valuation leads to higher price and profit.

• Reduced Consumer Heterogeneity While consumer valuations for the simple product is

state dependent, their valuations for the feature rich product is state independent. Put dif-

ferently, the feature rich product provides ‘consumption insurance’ in every state of nature.

When consumers are heterogeneous with respect to their beliefs but not on the features’

ex-post usage value, uniform pricing is a more effective tool to extract consumer surplus.

14

costvaluation = γ

(1+θm in

2 )(v−v)

θ min

Product Choice: Single−Product Monopoly

0 0.5 1 1.50.1

0.55

1

Product b

Product ab

The following proposition characterizes firms’ product decision.

Proposition 1. A single-product monopoly offers the simple product b if (1−θmin)(v− v) > γ +

c− c, and offers the feature rich product ab otherwise.

Figure 1 illustrates the above proposition. We plot the optimal decision as a function of

θmin (consumer heterogeneity) and γ(1+θmin)(v−v)/2 (the ratio of complexity cost to average feature

value).

As figure 1 illustrates, the monopolist is more likely to offer the feature rich product when

the cost/value ratio is low and θmin is small. Smaller cost/value ratio and smaller θmin both lead

to greater option value, and the heterogeneity reduction effect is strong when consumers are more

heterogeneous (as in the case of small θmin).

Observation 1. A single-product monopoly may offer the feature rich product even when

γ(1+θmin)(v−v)/2 > 1.

One interesting observation is that the firm may offer the feature rich product even if the

cost/value ratio is greater than 1. This represents a case where the large complexity cost over-

15

weights the option value associated with more product features. This result is driven by the hetero-

geneity reduction effect of feature-rich product, which provides the same expected consumption

value regardless of consumer uncertainty. Although total consumer surplus becomes smaller due

to the complexity cost, the firm can extract surplus more effectively. The intuition behind this find-

ing is similar to that in the bundling literature (Bakos and Brynjolfsson 1999, McAfee et al. 1989),

where the bundling of multiple product reduces the heterogeneity in consumer willingness to pay.

The intuition carries through even when seperate markets for the product features do not exist.

One caveat applies to the above results. While feature rich product provides option values

as long as consumer are uncertainty about feature need, the heterogeneity reduction effect is driven

by our assumption that the consumers are heterogneous with respect to their uncertain beliefs,

but they agree on the usefulness (i.e., value) of a feature once they know they need it. As a

result of this assumption, consumers have state-independent and homogeneous valuations of the

feature rich product. While this assumption might not hold exactly in reality, we believe that

the heterogneity reduction effect of feature rich product will remain true (albeit weaker) in most

situations, especially when the number of feature is large 6. The important take-away is that the

feature rich product is not only offered to enhance consumer valuation, but also for pricing reasons.

4.1.2 Anecdotal Observations

The single-product monopoly is perhaps an oversimplified description of real world scenarios. The

single-product monopoly model, nevertheless, sheds light on the basic trade-off driving the firm’s

product features decision. Recent press stories provide an abundance of cases where companies

make major effort in reducing their product features, after keeping adding features for many years.

An examination of these stories reveal on interesting pattern. The process of including more and

more features is often an effort to accomodate diverse or conflicting consumer needs, while the

decision to reduce features is often a result of accumulating consumer complains about feature

6see (Bakos and Brynjolfsson 1999) for an argument in a similar context, based on the Central Limit Theorem

16

need.

A dominant market leader is often observed in the software markets, which exhibit strong

network effects. Consider the case of the voice-call software, Skype. Skype 1.0 was a tremendous

success praised for its extreme simplicity and great functionality. In the subsequent versions, the

development team started to incorporate more features and third-party ‘Skype Extra’ applications

in the software. As the complexity grows, consumer complaint has been heard. In a dialogue7

between a few Skype users and an engineer from the development, the users complained about

too many unused features, which consumed system resource and lowered the reliability of simple

voice calls. The development engineer, on the other hand, defended these features by claiming that

the features are incorporated because of stated needs from users. Moreover, potential consumers

seem to be in support of more product features when they are surveyed. Nevertheless, in response

to the users’ demand for simplicity, Skype has started reducing the number of features in the most

recent versions. It has terminated the ‘Skype Extra Developer’ programs and taken out some of the

in-house developed applications.

4.2 Single-Product Duopoly

4.2.1 Equilibrium Results

To model firm competition, we consider heterogeneous consumers with respect to both feature

need and brand preference. We first provide intuitions on the consumers’ choice problem, which

drives the firms’ product decisions.

When competing firms offer different products, consumer choices are driven by both brand

preference and feature need. The relative magnitudes of t and v− v determine which factor will

drive consumer choice in equilibrium. When t is relatively large, brand preference is the major

factor driving consumer choices. The firms divide the market along the horizontal dimension

7http://forum.skype.com/index.php?showtopic=92264

17

regardless of their product choice. In equilibrium, the consumers who prefer one brand strongly

(x=1) will never buy the other brand regardless of the product feature. Similarly, when v− v is

relatively important, feature need will drive consumer choices in equilibrium. If both the simple

product and the feature rich product is offered, an uncertain consumer will never buy the simple

product regardless of its brand. The above observation is formalized in Lemma 2 in the appendix.

When firms are in competition, they seek to maximize the degree of product differentiation.

When brand preference is not sufficiently important, firms can engage in uncertainty-based seg-

mentation: one firm sells ab to the uncertain consumers, while the other firm sells b to the certain

consumers. As such, we identify two major incentives for a firm to offer the feature rich product:

• Option Value: When brand preference is driving consumer choices, offering the feature rich

product does not increase product differentiation, but may lead to higher consumer valuation

and therefore larger market share.

• Uncertainty-Based Segmentation: When feature need is driving consumer choice, firms

can increase differentiation by offering different feature configurations. The feature rich

product is able to target the uncertain consumers when the competitor offers the simple

product and targets the certain consumers.

Overall, the equilibrium strategy choice is described by Proposition 2.

Proposition 2. In a duopoly case, the equilibrium outcome is determined by three parameters:

θmin,β = v−v,γ . The second parameter is the relative importance of feature need (compared with

brand differentiation). The equilibrium is characterized by the following:

When feature need is not important, firms will offer identical products,

(a) Both firms will offer the feature rich product (ab) if the complexity disutility is small;

(b) both firm will offer the simple product b if the complexity disutility is large;

18

When feature need is important, firms will differentiate by including different features in

their products.

(c) When θmin is small or complexity disutility is large, firms offer simple products with different

features;

(d) When θmin is large and complexity disutility is intermediate, firms offer the simple product

and the complex product respectively.

γ: complexity disutility

v−

v:

feat

ure

valu

e

c = c = 1, t=2, θmin = 0.75

1 2 3 4 5 6 7 8 9 10

5

10

20

30

40

ab−b: uncertaintybased segmentation

Both firms offer b

Bothfirmsoffer ab

γ: complexity disutility

v−

v:

feat

ure

valu

e

c = c=1, t=2,θmin=0.5

2 4 6 8 10

10

20

30

γ: complexity disutility

v−

v:

feat

ure

valu

e

c = c=1, t=2,θmin=0.1

2 4 6 8 100

5

10

a−b: feature needbased differentiation

a−b: feature needbased differentiation

Both firms offer bBoth firms offer b

Bothfirmsofferab

Bothfirmsofferab

ab−b: uncertainty based differentiation

Figure 2: Equilibrium Product Offering of a Single Product Duopoly

The exact ranges where each outcome occurs as equilibrium are given in the appendix.

19

Figure 2 illustrates the above proposition. Our analysis reveals that there are two situations where

the ab product is offered in equilibrium. Interestingly, we find that offering feature rich product

may be either a prisoner’s dilemma outcome or a socially efficient equilibrium.

Observation 2. The ab−ab equilibrium is a prisoners’ dilemma situation: both firms are better

off if they offer the simple products, but ’both overload’ emerges as an equilibrium outcome. The

ab−b equilibrium maximizes joint profit.

Firms will offer the feature rich product in two types of equilibrium. In the ’both ab’

equilibrium, feature need is less important compared to brand preference, and complexity disutility

is low. As a result, brand preference will be driving consumer choice. The firms cannot increase the

degree of differentiation by offering products with different feature configurations. Compared to

the simple product, the feature rich product receive lower valuation from the certain consumers but

higher valuation from the uncertain consumers. When complexity disutility is small, the benefit of

offering the feature rich product will exceeds the cost. As a result, both firms offer the feature rich

product in equilibrium. The benefit from the feature rich product will be competed away, and the

equilibrium profit is no more than the profit if both firms offer the simple product.

In the uncertainty based differentiation equilibrium, firms offer ab and b respectively. The

joint profit is maximized in equilibrium. Said differently, when feature need is important, uncer-

tainty based segmentation is more effective than the horizontal brand differentiation. This result

leads to the following observation.

Observation 3. For some parameter combination θmin,β , there exists γ1 > γ2, where the equi-

librium profits for both the competitors are higher with γ1. Profits are increasing in complexity

disutility.

The above result indicates that complexity cost can actually increase firm profits in the

competitive case. This counterintuitive result takes place when the feature need is important com-

20

pared to brand preference. In this scenario, the joint profit is maximized when firms engage in

uncertainty based segmentation by offering the simple product(b) and the feature rich product(ab)

respectively. However, without complexity disutility, b−ab differentiation corresponds to a case

of vertical differentiation. All the consumers strictly prefer the feature rich product. As such, the

profit is not divided evenly between the competitors. The simple product is perceived as inferior to

the feature rich product. Consequently, ab−ab emerges as an equilibrium, leading to a prisoner’s

dilemma situation.

Complexity disutility qualitatively change the nature of feature based differentiation. It

turns the ab−b vertical differentiation into a type of horizontal differentiation. While the uncertain

consumers still prefer the feature rich product, the most certain consumers will find the option value

from the additional feature too small to justify the complexity cost associated with the feature rich

product. In equilibrium, the competitors can segment the market based on consumer uncertainty,

wherein the simple product is perceived as different from the feature rich product. This leads to

higher profits for both firms.

4.2.2 Anecdotal Observations

The above analyses resonate with the anecdotal evidences from many product markets. In many

markets of high tech products, adding features become more and more feasible with the progress

in design and manufacturer technologies. Firms start to compete by keeping up with each others’

product features. In the MP3 player category, for examples, leading competitors offer largely

similar product lines in terms of the feature configuration. The pairwise correlations between the

product lines of four leading brands range from 0.86 to 0.98 8. Our analysis reveals that this may

8We acquire product line data from a consumer review website, epinions.com. We consider the top four featureswhich most number of products include. Thus, there are sixteen possible products in terms of feature configuration.The product line of each firm is defined by a distribution function of their products over the feature configurations.The correlation between the product lines of two brands equals one if for every possible feature configuration, the twofirms have identical percentage of their products. The correlation is zero if two firms offer completely non-overlappingproduct lines.

21

be considered a ‘feature arm-racing’ pattern of product competition, and may be detrimental for

all competitors.

As firms put more and more features into their products, complexity and usability become

a more important consideration. In many markets, consumers have clearly divided preferences

for simple and feature rich products. Consider a discussion thread hosted on Google community

9 about Google talk vs Windows Messenger. The participants gave reasons why they favored

different IM messengers. Most people favor GTalk because its ‘simple interface’, and some favored

Windows Messenger because it has ’nice features’, ’emoticons’ and ’video call’ function.

Our analysis suggests a case where firms’ profits raise as a result of higher complexity

disutility. This result partly depends on the assumption of a duopoly without entry. In practice,

we often observe that as products become more sophisticated and complexity cost becomes a

greater concern, startups or incumbents spot novel market oppurnities by introducing extremely

simple products. In 2004, the Swiss mobile operator, Orange, launched a three-button mobile

phone called Mobi-Click or ’The mobile for grandparents’. The model was a huge success among

the elder people: consumers who are certain about their feature need and who find complexity

extremely costly. Released in 2006, the Flip camcorder by Pure Digital is an example of simple

product that has hit a mass market success. The company’s best selling model, Flip Extra, has

been praised as the “World’s simplest video camera” and won a market share of 13 percent in

2007. Revenue at Pure Digital grew more than 44000 percent by the end of 2008, the highest

rate among Silicon Valley firms. In more and more product categories, both entrants and market

incumbents start to use simplicity as a key point of differentiation.

9http://www.googlecommunity.com/about9101-0-asc-0.html

22

4.3 Multi-Product Monopoly

Next, we analyze the product line decision of a multi-product monopoly. The major insights from

the single-product case remain valid. In addition, our analysis reveal an additional driver to a

multi-product firm’s decision to offer the feature rich product, which we call Uncertainty Based

Price Discrimination. Recall from the previous sections, when consumers have heterogeneous

belief about the state of nature, they also have heterogeneous preference about the simple product

versus the feature rich product. When the feature rich product is offered together with the simple

product, the consumers will self select to purchase their preferred product. To summarize, the firm

has three major incentives to offer the feature rich product:

• Option Value The firm offers the feature rich product because all product features provide

consumers option values. Higher valuation leads to higher price and profit.

• Consumer Heterogeneity Reduction While consumer valuations for the simple product is

state dependent, their valuations for the feature rich product is state independent, since the

feature rich product provides the needed feature in every state of nature. State independence

leads to more homogeneous preference when the consumers are heterogeneous with respect

to their preference uncertainty.

• Uncertainty Based Price Discrimination The feature rich product is offered together with

the simple product, allowing better market segmentation.

We describe the firm’s product line decision in the following proposition. When launching

products are costless, the firm always offers a full product line. Thus, we introduce Fab, the fixed

cost of offering the feature rich product, into the analysis. To illustrate the Uncertainty Based Price

Discrimination effect, we compare the case where the firm can observe the belief of each consumer

to the case where types are unobservable. In the later case, the firm has stronger incentives to offer

multiple products.

23

Proposition 3. Denoting Fab as the fixed cost of introducing the feature rich product, the multi-

product monopolist’s product line decision can be described by the following table:

Fixed Cost of ab Fab <C1 C1 < Fab <C2 C2 < Fab

Product Decision:Introduce {ab} or Not?Types Observable Yes No No

Types Unobservable Yes Yes No

C2 is the incremental benefit of offering ab when consumer type is unobservable, and C1 is

the incremental benefit of offering ab when price discrimination is possible. C2 >C1 and the exact

expressions are provided in the appendix.

When the fixed introduction cost is intermediate, the option value alone is insufficient to

justify the introduction of product ab. However, better price discrimination makes the feature rich

product valuable. Thus, the firm has greater incentive to offer the feature rich product when price

discrimination is not possible, since offering a menu of products helps the firm to screen consumers

based on their uncertainty about feature need, which facilitates price discrimination. In such cases,

the firms may charge lower price for the feature rich product. The above finding is not surprising

considered in the context of the economics literature of asymmetric information and screening. We

argue that offering the feature rich product can be a mean to screen the consumers based on their

feature need uncertainty.

Observation 4. The firm may charge either a higher or a lower price for the feature rich product,

compared to the simple product.

When the firm offers multiple product with different feature configurations, the feature

rich products may not necessarily be considered superior and sold at higher prices. Instead, it

is considered as a ‘generalist’ product and comes cheaper than the ‘specialist’ product which is

perceived to excel on a certain dimension. This resonates with the empirical findings by Chernev

24

(2007). The result also points to one of our recurring theme: the feature rich product may not lead

to higher willingness to pay on the consumer side. Insead, it may be offered for pricing reasons.

4.4 Multi-Product Duopoly

In this section, we model the product line rivalry (Brander and Eaton 1984, Gilbert and Matutes

1993, Verboven 1999) of multi-product firms. In the first stage of the game, the firms choose their

product portfolio. There are 23 = 8 possible product portfolios, and each product portfolio is a

subset of {a,b,ab}. In the second stage, firms simultaneously set prices for all their products and

consumers decide which product to buy. In the third stage, consumers learn their feature need and

get consumption benefits.

When firms offer several products, the substitution pattern is much more complicated.

When a firm lowers the price of a certain product, it not only steals customers from the com-

petitor, but also attracts the buyers of other products in its own product line. This intra-firm profit

interaction (Cabral and Villas-Boas 2005) will creates a more complicated scenario, especially

in our asymmetric setting. The complete analysis of the multi-product equilibrium is technically

prohibiting. Instead, in previous studies (Brander and Eaton 1984, Gilbert and Matutes 1993) on

multi-product competition, the authors focused on the following questions: whether the compet-

ing firms will offer interlaced product lines or identical product lines? In the context of feature

configuration, we provide answer to this question in the following proposition.

Proposition 4. When the ratio v−vt is sufficiently large, the firms will offer non-overlapping product

lines. The equilibrium product line configuration is similar to the single product line case. In

particular, firms offer ab and b products respectively when γ is not too large and θmin is close to 1.

Otherwise, the firms offer a and b product respectively.

When the ratio v−vt is sufficiently small, the firms will offer identical product lines. In

particular, both firms will offer a, b and ab if the complexity cost is small and θmin if small.

25

As expected, the above results confirm the main insights from the single product section.

In particular, we find that competing firms may either offer identical product lines and differentiate

on the ‘brand’ attribute, or offer products with different features. In the later case, simplicity may

imply higher quality for some consumers, and may be used as a point of differentiation.

5 Measuring Consumer Uncertainty: Method and EmpiricalEvidences

In this section, we intend to provide an empirical examination of our modeling assumptions. While

the existence of complexity disutility is well supported by the previous behavior literature, feature

need uncertainty and the heterogeneity in such uncertainty have not been illustrated. Since these

assumptions drive our equilibrium results, we develop a measurement technique that attempts to

quantify feature need uncertainty and provide empirical supports to the heterogeneity of feature

need uncertainty in a field study.

We provide a brief description of the method and the results from a field study. Details

about the methodology as well as simulation validation results are delegated to the appendix.

5.1 Description of the Method

In our theoretical model, we assumed that the consumers will need one and only one of two fea-

tures. This was a deliberately made choice to illustrate that firms may include two features into one

product even if it’s common knowledge that no consumer will need both features simultaneously.

In this section, we allow that consumers have independent need for each feature, so that they may

need both features at the same time or neither feature at all.

We propose a preference measurement method that explicitly estimate the consumers’ un-

certainty about feature needs. The method is based upon the standard conjoint analysis methodol-

ogy. We focus on estimating a linear utility function, such as:

26

ui j =k=K

∑k=1

θik ∗ wik ∗ x jk −wi

p ∗ p j + εi j

where x j1 to x jK are binary variables for the levels of features, and p j is the price of the jth

option. win is the ex-post usage value of feature k. θin is the probability that the consumer will need

the feature or the estimated long term usage frequency 10. When consumer is uncertain about his

feature need, win = θin∗ wi

n corresponds to the expected value of the feature. The term εi j represents

a random error term.

Based on revealed preference data, such as stated choice between different product pro-

files, traditional conjoint analysis methods are able to estimate win for each feature. However,

disentangling θin and win becomes challenging. Put differently, it is difficult to infer ex-post feature

valuation (e.g., usage value) and feature need uncertainty from the estimated option value.

In order to disentangle the option value of a product feature and the ex-post usage value,

we introduce two types of product profiles into the choice questions. In the first type of product

profiles, the product is described by its built-in features and a price; in the second type of product

profiles, the features are explicitly offered as options, which is not included in the base product, but

available as an upgrade. By observing a consumer’s stated choices between product profiles, we

infer his ex-ante willingness to pay for the built-in feature, and his ex-post willingness to pay for the

upgrade. When consumers are uncertain about their feature need, the ex-ante WTP is lower than

the ex-post WTP. We can infer the feature need uncertainty from this discrepancy in willingness to

pay.

In the appendix, we provide a detailed description of the preference models and the esti-

mation method which underly the above intuition. The methodology has three key features:

• When a product feature is offered as an option, the consumer may or may not exercise (e.g.,

10The long term usage frequency results from either uncertainty about usage in each period (As in Guo (2006)) orinfrequent but certain needs. These represent cases that are mathematically equivalent in our framework

27

upgrade) it even if he needs it. We predict the (post purchase) upgrade decision based on a

given set of utility parameters.

• Based on the predicted usage decisions, we infer the ex-ante utility and predict the choice

behaviors, based on a given set of utility parameters.

• By minimizing the deviation of the predicted choices and the observed choices, we estimate

the utility parameters, including consumer uncertainty and the ex-post WTP.

As a methodological note, our method is closely related to the Discrete/Continuous choice

models in economics (Hanemann 1984) and marketing (Iyengar, Jedidi, and Kohli 2008). In the

Discrete/Continuous framework, the researcher observes the consumers choice behavior but not

the post-choice consumption decision (which is usually represented by a Continuous variable).

By assuming that consumers make consumption decisions to maximize utility conditional upon

choice, the researcher is able to estimate utility parameters and predict both choice (Discrete) and

consumption (Continuous) decisions of the consumers. In our framework, we treat the post-choice

feature upgrade decision as a binary random variable and model its impact on choice. When the

consumer uncertainty parameter θ is treated as a continuous consumption variable, there exists a

mathematical equivalence between our method and that developed by Iyengar et al. (2008)11.

5.2 Field Study

In this section, we briefly present some initial results from a field study. We measure consumer

uncertainty about several value-added services in mobile phone plans based on a standard CBC

study. We include a total of four non-price product features: ’unlimited video and picture mes-

sages’(MMS), ’the MobiTV service’, the ’Mobile E-mail PLUS’ feature, and the ’Happy Roamer’

11A formal illustration is out of the scope of this paper. We refer interested readers to the appendix and the method-ology section of Iyengar et al. (2008) for detailed information

28

feature. For detailed description of these features, please refer to the appendix. We measured con-

sumer uncertainty about the ’Mobile E-mail PLUS’ feature and the ’Happy Roamer’ feature. A

priori, we conjecture that consumers are uncertain about both features.

All the product features have two levels, 1 when the feature is included in the plan, 0

otherwise. Price has two levels for the basic service plan. When any of the feature is offered as an

upgrade option, there is an upgrade price to be paid. The upgrade price has three levels.

The service plan requires a one year contract. Thus, the consumers cannot switch between

different service plan during the course of usage. They can only obtain consumption flexibility by

including the feature upgrade option. For the briefing text for the choice questionnaire, please refer

to the appendix.

We estimate consumer part-worth based on eight questions without the upgrade option. In

the second stage, we use four questions each to measure consumer uncertainty about the above-

mentioned features. The two features are never offered both as upgrade options simultaneously.

We present the estimation results in the following tables:

Attributes: MMS MobiTV Mobile E-mail Plus Happy Roamer PriceEstimated Partworth 0.1317 0.2473 0.6577 0.5896 -0.3759

Table 5. Estimated Partworths

Table 5 presents the population average of the estimated partworth. Overall, the subjects

find Mobile E-mail PLUS and the Happy Roamer as the most important features. The negative

price parthworth means a lower utility when the price level is 1 (high price).

Based on these parthworth, we estimate consumer uncertainty in the second stage esti-

mation task. The data exhibit two stylized patterns: First, for otherwise identical options, many

subjects prefer the option where the feature is offered as an upgrade, even if the total price (e.g. the

price paid if the upgrade is purchased every month.) is higher for this option. Second, for options

29

offered at the same total prices, many subject prefer the option where the feature is offered as an

upgrade, even if this option has less other features (for example, MobiTV or MMS).

The population average of the belief parameter (θ ) is presented in table 6. According to

these estimations, the consumers are more likely to use the Mobile E-mail PLUS feature than the

Happy Roamer feature.

Attributes: Happy Roamer Mobile E-mail PLUSEstimated Uncertainty parameter θ 0.33 0.47Variance of uncertainty parameter θ 0.07 0.11

Table 6. Estimated Uncertainty Parameter θ

These estimations reflect that the consumer expect to use the Mobile E-mail feature more

frequently than the Happy Roamer feature. This agrees with our interviews with a few subjects.

This leads to the difference between ex-ante valuation and ex-post valution, which we present in

table 7.

Ex-ante Valuation Ex-post ValuationMobile E-mail PLUS 1.67 5.13

Happy Roamer 1.49 6.65

Table 7. Difference in Ex-ante and Ex-post valuations

We present the population averages of the ex-ante valuations and ex-post valuations (con-

sumption value). The consumption values are higher than the ex-ante valuations. The difference

is significant (p < 0.001) for both Mobile E-mail PLUS and Happy Roamer. Interestingly, due

to a higher likelihood to be used, the Mobile E-mail PLUS feature is more important ex-ante, but

consumers value Happy Roamer more when they do need it. The results presented in Table 6 and

Table 7, taken togather, point to the validity of our first model assumption:

30

<0.3 0.4 0.6 0.8 >0.80

2

4

6

8

10

12

14

θ

Fre

quency

0 <0.3 0.4 0.6 0.8 >0.80

2

4

6

8

10

12

14

16

18

20

θ

Fre

quency

θ Happy Roamerθ Mobile Email

Figure 3: Population Distributions of the θ Parameter

• There exist consumer uncertainty about feature need. The ex-ante willingness-to-pay is

significantly smaller than the ex-post willingness to pay when the feature is offered as an

upgrade-on-demand.

Our second assumption concerns the heterogneity of feature need uncertainty. The vari-

ances of uncertainty parameter θ are presented in Table 6. The consumers have more heteroge-

neous beliefs on the usage of the Mobile E-mail PLUS feature. In fact, their belief about the

Mobile E-mail PLUS pattern exhibits a multi-modal pattern, while the belief about Happy Roamer

is unimodally distributed wherein most consumers think it’s unlikely to be useful. The histograms

which represent the empirical distributions of θ are plotted in Figure 3:

The above results support our second utility function assumption:

• The consumers have heterogeneous uncertainty about their feature need.

The above results, although preliminary, provide some support for our chosen utility func-

tion. They support our central assumption of feature need uncertainty and the heterogeneity in such

uncertainty. Clearly, the empirical results also show that the distribution of belief parameter θ is

31

not uniform, which is a simplifying assumption made for technical reasons. The empirical context

(mobile service plan) was chosen because optional upgrade was considered nature and easier to

comprehend in this category. In future works, we plan to examine feature need uncertainty more

systematically across more product categories.

6 Conclusions and Limitations

In this paper, we developed a theory of ’feature overload’ to explain the widely observed practice

of including features in the products that consumers never use. We argue that even it is common

knowledge that none of the consumers ex-post know all the features, offering products with all the

features still merge as an equilibrium outcome. The option value of additional features appeals to

the uncertain consumers, and this leads to both higher valuation and better market segmentation.

In competition, offering feature rich products can happen both as a prisoner’s dilemma outcome

and as an optimal differentiation outcome.

Heterogeneous feature need uncertain stands as a central assumption in our theory and

drives most of our results. We develop a methodology that explicitly measures consumer uncer-

tainty in feature need. This enables us to provide some preliminary evidence on our modeling

assumptions in a field study.

Our study has a number of limitations. First, our model assumes a simple utility function.

While being consistent with the literature on preference uncertainty, this utility function doesn’t

capture several important factors: the potential richness of consumer belief, such as the correlation

between feature need; heterogeneity in complexity cost which depends on consumer expertise; and

heterogeneous ex-post valuations when a feature is needed. These factors are left out to simplify

exposition, and incorporating them represents fruitful directions for future research.

Second, we did not compare our theory with alternative theories on feature overload. While

our theory addresses the demand side, the feature overload phenomenon can be attributed to the

32

cost structure of the firm, the biases in firm level marketing research or the behavior of the new

product design engineers. Comparing these various theories will lead to a more comprehensive

understanding of the feature overload phenonmen.

The explosion of product features is a recent phenomenon especially proununced in the

high technology markets. This paper serves as a first study on this topic in an equilibrium frame-

work. A number of interesting issues remain unexplored. For example, the penetration of the

Internet leads to wide availability of both product and usage information. Consumers discuss their

usage experience in the on-line social sphere such as blogs and social networks, and professional

consumer education agencies such as ConsumerRerport.com is increasingly accessible. How con-

sumers feature need uncertainty, partly derived from the lack of consumption experience, interact

with this abundance of information? On the supply side, the advent of design modularity and mass

customization has enabled firms to offer much greater flexibility in terms of product feature provi-

sion, such as offering features as on-demand upgrades. How will such practices interact with the

firm’s product design approaches? We left these questions for future research.

References

Bakos, Yannis, Erik Brynjolfsson. 1999. Bundling information goods: Pricing, profits, and effi-ciency. MNSC 45(12) 1613–1630.

Biyalogorsky, E., E. Gerstner, D. Weiss, J. Xie. 2005. The Economics of Service Upgrades. Journalof Service Research 7(3) 234.

Brander, J.A., J. Eaton. 1984. Product Line Rivalry. American Economic Review 74(3) 323–334.

Cabral, L.M.B., M. Villas-Boas. 2005. Bertrand Supertraps. Management Science 51(4) 599–613.

Chernev, A. 2007. Jack of all trades or master of one? Product differentiation and compensatoryreasoning in consumer choice. Journal of Consumer Research 33(4) 430–444.

Della Vigna, S., U. Malmendier. 2006. Paying Not to Go to the Gym. American Economic Review96(3) 694–719.

Gilbert, R.J., C. Matutes. 1993. Product Line Rivalry with Brand Differentiation. JOURNAL OFINDUSTRIAL ECONOMICS 41 223–223.

33

Guo, L. 2006. Consumption flexibility, product configuration, and market competition. MarketingScience 25(2) 116–130. doi:10.1287/mksc.1050.0169.

Hanemann, W.M. 1984. Discrete/continuous models of consumer demand. Econometrica: Journalof the Econometric Society 541–561.

Hauser, J.R., B. Wernerfelt. 1990. An evaluation Cost Model of Consideration Sets. Journal ofConsumer Research 16(4) 393.

Iyengar, R., K. Jedidi, R. Kohli. 2008. A Conjoint Approach to Multipart Pricing. Journal ofMarketing Research 45(2) 195–210.

Klemperer, P. 1992. Equilibrium product lines: Competing head-to-head may be less competitive.The American Economic Review 740–755.

Lambrecht, A., K. Seim, B. Skiera. 2007. Does Uncertainty Matter? Consumer Behaviour UnderThree-Part Tariffs. Marketing Science 26(5) 698–710.

Matutes, Carmen, Pierre Regibeau. 1992. Compatibility and bundling of complementary goods ina duopoly. The Journal of Industrial Economics 40(1) 37–54.

McAfee, R. Preston, John McMillan, Michael D. Whinston. 1989. Multiproduct monopoly, com-modity bundling, and correlation of values. The Quarterly Journal of Economics 104(2)371–383.

Meyer, Robert J., Shenghui Zhao, Jin K. Han. 2008. Biases in Valuation vs. Usage of InnovativeProduct Features. MARKETING SCIENCE 27(6) 1083–1096. doi:10.1287/mksc.1080.0367.

Mukherjee, A., W.D. Hoyer. 2001. The Effect of Novel Attributes on Product Evaluation. Journalof Consumer Research 28(3) 462–472.

Nalebuff, BJ. 2000. Competing Against Bundles. Tech. rep., Working Paper Series, Yale Schoolof Management.

Narayanan, S., P.K. Chintagunta, E.J. Miravete. 2007. The role of self selection, usage uncer-tainty and learning in the demand for local telephone service. Quantitative Marketing andEconomics 5(1) 1–34.

Shin, J., D. Ariely. 2004. Keeping doors open: The effect of unavailability on incentives to keepoptions viable. Management Science 575–586.

Simonson, I., Z. Carmon, S. O’Curry. 1994. Experimental Evidence on the Negative Effect ofProduct Features and Sales Promotions on Brand Choice. MARKETING SCIENCE 13 23–23.

Stremersch, S., G.J. Tellis. 2002. Strategic Bundling of Products and Prices: A New Synthesis forMarketing. Journal of Marketing 66(1) 55–72.

34

Thompson, D.V., R.W. Hamilton, R.T. Rust. 2005. Feature fatigue: when product capabilitiesbecome too much of a good thing. Journal of Marketing Research 42(4) 431–442.

Verboven, F. 1999. Product Line Rivalry and Market Segmentation-with an Application to Auto-mobile Optional Engine Pricing. Journal of Industrial Economics 47(4) 399–425.

Villas-Boas, J.M. 2004. Consumer learning, brand loyalty, and competition. Marketing Science23(1) 134–145.

Xie, J., S.M. Shugan. 2001. Electronic Tickets, Smart Cards, and Online Prepayments: When andHow to Advance Sell. Marketing Science 20(3) 219.

Xie, Jinhong, Eitan Gerstner. 2007. Service Escape: Profiting from Customer Cancellations. Mar-keting Science 26(1) 18–30.

35