Performance and Survival Implications of Exploration and Diversification

Jamie P. Eggers 3024 Steinberg Hall - Dietrich Hall

Wharton School Philadelphia, PA 19104

tel: (215) 746-3111 e-mail: jpeggers@wharton.upenn.edu

Nicolaj Siggelkow 2211 Steinberg Hall - Dietrich Hall

Wharton School Philadelphia, PA 19104

tel: (215) 573 7137 e-mail: siggelkow@wharton.upenn.edu

Performance and Survival Implications of Exploration and Diversification

Abstract: To study the effects of product portfolio decisions on firm performance and survival, this paper distinguishes between diversification – a measure of product breadth – and exploration – a measure of the degree to which a firm’s product diversity cannot be explained by random product placement. Using a data set that includes all U.S. mutual fund providers and the funds they offered between 1962 and 2002, we find a curvi-linear relationship between exploration and firm performance, with firms that are either highly exploitative or highly exploratory outperforming firms that are “stuck in the middle.” Similar results are obtained for diversification. While diversification shows an identical curvi-linear effect on survival, no effect of exploration on firm survival can be detected. Thus, product placement that deviates from random product placement within an industry appears to have no survival implications, indicating a limit to the effects of purposeful managerial action.

1

INTRODUCTION

In multi-product firms, managers need to make decisions about the location of new products.

Should new products be closely related to existing ones, or should new products cover market

niches not currently served by the firm? While studies of inter- and intra-diversification have

revealed the firm-level effects of product diversity per se, these studies often beg the question

whether a firm could have achieved the same outcome by random choice of product markets. For

instance, while a broad product portfolio might increase the likelihood of a firm’s survival

(Fleming and Sorenson, 2004), it remains an open question whether portfolio breadth still has an

effect once one controls for the degree of diversification that would be expected if the firm had

placed its new products at random. In other words, does the care that goes into making choices

about where to place new products make a real difference to firm-level outcomes?

To make progress on this issue, we distinguish in this paper between “diversification,” a

measure of product diversity, and “exploration,” a measure of the degree to which a firm’s

product diversity cannot be explained by random product placement. We argue that exploration,

thus operationalized, captures managerial choices about intra-industry market exploration to a

better degree than the diversification measure alone. Using data on all U.S. mutual fund

providers that existed over the period 1962-2002, we empirically investigate the role of

diversification and exploration on firm performance and firm survival, thereby examining

potential boundaries to the effects of managerial intention.

A Better Understanding of Choice

Accurate measures of managerial action and decision-making are difficult to come by. In the

context of product portfolio choices, one might argue that a diversification measure captures the

outcomes of the underlying decision processes. This argument would sidestep, however, the

2

question of the relevant benchmark. From what degree of diversification (or focus) could one

infer that indeed a purposeful, underlying decision process was in place? We argue that

deviations from a random benchmark, i.e., from the degree of diversification that would arise had

the firm randomly chosen product categories, plausibly point to conscious decision processes.

We call this deviation exploration, as March (1991) links exploration to search, risk taking,

experimentation, flexibility, discovery and innovation – concepts that are endemically linked to

managerial decision-making and control. In a multi-product, single-industry context, firms that

make new product placement decisions with little regard to their existing product base (and thus

approach or even surpass the random benchmark introduced above) potentially risk falling into

March’s (1991) appraisal of over-exploration, where firms “exhibit too many underdeveloped

new ideas and too little distinctive competence.” Alternatively, firms that repeatedly choose to

place new products in close proximity to existing products (and thus deviate negatively from the

random benchmark) may be able to refine a limited set of capabilities, but may not be in a

position to develop new ones. While individual decisions or activities have often been

characterized as either exploratory or exploitative (Koza and Lewin, 1998; Rosenkopf and

Nerkar, 2001; Rothaermel and Deeds, 2004), looking at the accumulation of previous choices by

a firm yields a portfolio that is exploratory, exploitative, or balanced. In fact, it may even be

more helpful to look at the portfolio as a whole, as the interaction between the different decisions

over time will be of potential interest (Vassolo, et al., 2004). This view of exploration is not

identical to the concept of diversification, which is characterized by the condition of being varied

and presenting a number of different options, without necessarily any implications about the

decision-making process that has lead to that state and without regard for the fact that firms that

offer a larger number of products will almost certainly offer a broader choice of products.

3

Indeed, we find that while diversification and exploration measures have fairly similar impacts

on firm performance, the two yield quite different results when studying firm survival.

To better understand the difference between a traditional measure of diversification and our

measure of exploration – particularly with respect to measuring the impact of managerial action

related to product placement – consider the following simple, hypothetical example. Assume an

industry has three broad product categories; within each broad category are two subcategories,

each with two specific product types. There are, therefore, 12 (= 3*2*2) product types arranged

in a simple, three-tiered hierarchical structure. Consider two different firms, a “Focused” and a

“Broad” firm, that each grow from one initial product to six products, each of a different type.

While each firm introduces its new products into product classes that it has not previously

explored, the Focused firm locates each new product as close to the existing products as possible.

The Broad firm places its products as far apart in the product space as possible with each new

introduction (see Figure 1).

To understand how broad or narrow the portfolio of products offered by each firm is, we use

the traditional concentric diversification index (Caves, et al., 1980). This measure of

diversification represents how broadly diversified a firm’s existing portfolio is at any given time,

taking into account the different degrees of relatedness among the various products. Our measure

of exploration subtracts from this diversification measure an estimate of how diversified a firm

would be if it chose product types at random, without regard for the existing product mix. This

measure captures the degree to which a firm is either exploring beyond its expected boundaries

or intentionally exploiting its existing product base. Figure 2 shows the values of the two

measures – diversification and exploration – for the Focused and the Broad firm as each firm

increases the number of its products from one to six.

4

This example highlights three points. First, as one would expect from the measure of

exploration, exploration is negative for the Focused firm (the firm is less diverse than would be

expected through random behavior) and exploration is positive for the Broad firm (the firm is

more broadly dispersed than one would expect of a randomly diversifying firm). Such a clear

distinction between the Broad and Focused firms does not exist for diversification.

Second, while both diversification and exploration capture the fact that the Focused firm has

a narrower portfolio than the Broad firm, the diversification measure for both the Focused and

the Broad firm increases sharply as the size of the firm increases, while the exploration measure

is less correlated with the number of products offered. (In this particular example, the correlation

between diversification and number of products is 0.84 while the correlation between

exploration and number of products is 0.12.) Thus, diversification appears to be very size-

dependent, making it likely that in a sample of firms, larger firms will generally appear to be

more diversified than smaller firms. Yet consider the broadly diversifying firm, when this firm

has two products vs. when it has six products (see again Figure 1). When it has two products,

these products are maximally different (they belong to different highest-level categories). When

it has six products, products are located in each broad product category. While it makes sense to

call the firm with six products “more diverse” (and indeed its diversification index is 55%

higher), it would be much less intuitive to equate this diversity with “higher exploration.” Having

two products in maximally different categories is a substantial indication of exploratory

behavior, and it is not clear why having six equally spaced products would necessarily

correspond to more exploratory behavior. Indeed, as shown in Figure 2, the value of exploration,

which controls for the random benchmark, is very similar for these two firms.

5

Third, the measures exploration and diversification may move in opposite directions if a firm

adds products that are highly related to existing products. In the example, this effect can be seen

most clearly for the Focused firm, as the firm moves from one to two products. While the

diversification measure increases, reflecting the broader array of products offered, the

exploration measure decreases, reflecting the close similarity of the second product to the first,

which would be unlikely to happen on a random basis. Again, the size dependency of the

diversification measure may mask an underlying pattern – in this case, a clear pattern of

exploitative behavior that is revealed by the exploration measure.

A Better Setting for Measuring Choice

Armed with this measure of product placement choice, we set out to understand the

implications of such choices for firms. At the inter-industry level, multiple streams of research in

management and finance have dealt with the question of how firms address important

developmental choices about selecting and adjusting the breadth of products and services they

offer. When considering the choice of exploring the product space broadly, or focusing on

exploiting a narrow range of products, studies of diversification (Rumelt, 1986; Montgomery and

Wernerfelt, 1988), related and unrelated acquisitions (Morck, et al., 1990), and spin-offs (Daley,

et al., 1997) have generally shown limits to the benefit of straying too far from a firm’s core

business. Most of these studies have, however, focused on the pros and cons of inter-industry

exploration – moving from an existing core business in one industry to a new business in a

different industry.

At the level of intra-industry diversification, prior work has shown that firms offering a

broader product array are able to garner higher market shares (Kekre and Srinivasan, 1990),

increase sales (Perloff and Salop, 1985), and improve their chances of survival (Sorenson, 2000;

6

Cottrell and Nault, 2004). There are, however, two significant limitations to the generalizability

and interpretation of these intra-industry results. First, these studies have been primarily

conducted in industries that show rapid technological change such as the personal computer

industry (Stern and Henderson, 2004), computer workstations (Sorenson, 2000), and personal

computer software (Cottrell and Nault, 2004). Given rapid technological obsolescence in these

industries, it would seem that in these empirical contexts the benefits of new product

introductions may be more closely aligned with the benefits of introducing new generations of

products and not necessarily tied to the benefits of exploring a broader array of products. Second,

these are all contexts in which exploration through the introduction of new products is very

costly, as it is tied to product innovation. Given these costs, it is unclear whether in these

industries firms survive and prosper because they explore, or whether they explore because they

are prosperous and have sufficient cash flow to fund innovation. Thus while this research

indicates a link between product exploration and high performance, the causality may run in

either direction.

To obtain a cleaner test of the effects of product portfolio management, it would be helpful to

have a setting in which firm survival and performance are not necessarily tied to the introduction

of new products and where the costs associated with such introductions are low. This would

involve a situation where all three options for growing a company are viable: growing revenue

from existing products without introducing new ones; introducing new products very similar to

existing products; and introducing new products that are very different from existing products.

This paper investigates the implications of intra-industry diversification and exploration for firm

performance – measured by both survival and financial measures – in a low-cost environment for

7

product introductions where all three of these strategies can be seen – the U.S. mutual fund

industry.

HYPOTHESES

To develop hypotheses concerning the effects of diversification and exploration on firm

performance and firm survival, we draw on five significant threads in the literature that each

have touched on various parts of our current inquiry: a growing literature on product

introductions, the resource-based view on diversification, the finance literature on focusing, the

marketing literature on one-stop-shopping, and work analyzing the tradeoffs between exploration

and exploitation.

The effects of diversification and exploration on firm performance

The literature on product introductions stresses the positive effect of broad product portfolios

on firm performance. It is argued that customers are attracted to “one-stop shopping” (Sheth,

1983; Kaufman and Lane, 1996). Moreover, customers are able to build a relationship with one

specific firm, and come to depend on that firm. As a result, it is expected that firms offering

broader portfolios will outperform other firms. Indeed, firms possessing broader portfolios of

products have been shown to have higher levels of market share (Kekre and Srinivasan, 1990).

These firms are also able to leverage those assets that are more broadly applicable, such as brand

names and advertising budgets, to maximize the benefit for a broad range of products.

At the same time, the extensive literature on diversification and focusing implies that firms

that stray too far beyond their initial bounds will be unable to replicate their success in new

arenas, and that customers will not react positively to the inability of a diversified firm to

compete with the performance of specialized, focused firms (Berger and Ofek, 1995). This

8

literature stresses the success of the other extreme, posing that specialist firms, focusing on a

narrow product range, are financially successful. The combination of these two perspectives

leads us to a “stuck in the middle” hypothesis for diversification, where firms at both ends of the

continuum outperform those in the middle of the spectrum. Firms at one end of the continuum

can benefit from deep subject-matter expertise that allows them to offer the most appealing

products; firms at the other end of the continuum can provide customers with the range of

products that they look for, while firms in the middle suffer the problems associated with

straying beyond their areas of expertise, without reaping the benefits of offering a one-stop-

shopping opportunity.

H1: The relationship between diversification and firm performance is U-shaped.

Work on the optimal amount of exploration generally stresses the benefits of a middle-

ground: Too little exploration may prevent a firm from finding the right product mix – given the

current environmental conditions and the firm’s capabilities – causing the firm to get stranded on

a “local peak” on its performance landscape (Levinthal, 1997). Conversely, too much exploration

may prevent a firm from developing deep expertise in any area and from exploiting its resource

and capability base, leading to overall instability and sub-optimal performance (Rivkin and

Siggelkow, 2003). In a similar vein, the work on the ambidextrous organization (Tushman and

O'Reilly, 1996) implies that the most successful firms generally have a balance of exploratory

behavior – acquiring new skills – and exploitative behavior – leveraging the new investment to

adequately develop and utilize the new skills. This tradeoff between exploitation and exploration

has a long history in a range of literatures. (See, e.g., the extensive work in the operations

research field on two-armed bandits starting with Robbins (1952), or, in the organizational

9

literature, the work by March (1991).) Overall, these arguments would lead to the following

hypothesis:

H2: The relationship between exploration and firm performance is inversely U-shaped.

Given that our empirical context is an intra-industry setting, it is a priori unclear whether

only the upslope portion of the inversely U-shaped curve might arise. Prior theory (noted above)

has been developed at a general level, so the boundary conditions on the shape of the relationship

are unclear. If one believes that exploration only becomes excessive once industry boundaries are

crossed, then one might expect that an intra-industry setting would return only the upslope. But it

is also possible that the theoretical arguments apply equally to intra- and inter-industry

environments, and that one would see a nonlinear relationship even within a single industry. Our

empirical analysis below will shed light on this question.

The effects of diversification and exploration on firm survival

Much of the recent work on product introductions and organizational survival (Sorenson,

2000; Cottrell and Nault, 2004; Stern and Henderson, 2004) has found that a broader portfolio of

products within a specific industry decreases the chance of firm failure. Broader product

portfolios increase the chance of survival, as they allow firms to diversify risk (Amihud and Lev,

1981), buffer competition through erecting entry barriers (Schmalensee, 1978), and engage in

mutual forbearance (Bernheim and Whinston, 1990). Thus, we would expect that firms with a

greater degree of diversification would be less at risk. At the same time, the perspective of the

resource-based view of the firm suggests that firms choosing to focus on their competencies

would be at a reduced risk of internal failure, as the process of diversification would necessitate

10

the acquisition of new routines and skills (Collis and Montgomery, 1997), a task which cannot be

achieved with certainty (Nelson and Winter, 1982). Thus, we would expect firms with a very

small degree of diversification would be at reduced risk of failure as well.

Posing that the performance and failure implications of diversification are mirror-images of

each other is also in agreement with the existing ecological view (Singh, et al., 1986) that

survival is a good proxy for firm performance, since it is a prerequisite of profitability.

Therefore, using the symmetry between performance and likelihood of survival, we would

additionally expect that very low levels and very high levels of exploration increase the

likelihood of failure. In sum, we hypothesize:

H3: The relationship between diversification and firm failure is U-shaped. H4: The relationship between exploration and firm failure is inversely U-shaped.

METHODS

Data

The U.S. mutual fund industry is the largest financial intermediary in the country, with more

than $5.6 trillion in assets at the end of 2002. Individual mutual funds are offered by mutual fund

providers, also called mutual fund families, such as Fidelity, Vanguard, and Merrill Lynch. In the

following, we use the terms “fund provider,” “family,” and “firm” interchangeably.

Our main data source is the Survivor Bias Free U.S. Mutual Fund Data Base maintained by

the Center for Research in Security Prices (CRSP) at the University of Chicago. The CRSP data

cover the history of virtually all open-end equity, bond, and money market funds that were

available between 1962 and 2002. The number of funds and families grew from 215 and 100 in

11

1962 to 15,524 and 478 in 2002, respectively. In all, there were 950 families that we documented

and on which we collected data, which existed at one time or another between 1962 and 2002.

Funds in the database have been classified into 181 categories by Strategic Insight, a mutual

fund research and consulting firm. A large number of these categories are comprised of state-

specific funds (e.g. Tax-Free-Bonds Alabama, Tax-Free-Bonds Arkansas, etc.). We reduced the

number of categories to 83 by combining such state-specific funds across states (e.g. the new

category “Tax-Free Single-State Bonds” contains the two funds mentioned above).

We further distinguish between different degrees of relatedness between fund categories. For

instance, an aggressive growth fund has more in common with a small company growth fund

than with a tax-free bond fund. We therefore organize the 83 categories into a four-level

hierarchical structure, similar to the SIC system with its 1-digit, 2-digit, etc., hierarchical

branching structure (see Appendix 1). We created this structure by grouping funds that invest in

similar securities together. To refine this structure, we also took the correlation among the

returns of the various categories into account. Thus categories whose returns are more highly

correlated also tend to be grouped more closely together.

At the highest level (level 1), we classify funds as belonging to one of three groups: equity,

bond, and money market. At the next levels we make ever finer distinctions. For instance, a

“Small Company Growth Fund” is classified as “Equity” (level-1), “Equity - General Domestic”

(level-2), “Equity - General Domestic - Aggressive Growth” (level 3), and “Equity - General

Domestic - Aggressive Growth-Small Company Growth” (level 4). This system of categorization

of relatedness is used to construct our primary independent variables, as described below. One

should note that whenever we refer to “categories,” we refer to categories at the lowest level of

aggregation, i.e., one of the 83 level-4 categories.

12

Probing into the CRSP data base revealed that the assignment of individual funds to mutual

fund providers involved a large number of problems. One set of problems revolved around faulty

assignments of fund provider identification numbers (e.g., the same fund provider was listed with

different identification numbers, or different fund providers had been assigned the same

number.) A second set of problems, less easily fixed, revolved around mergers and acquisitions

of mutual fund providers. To correct for these problems, we used publicly available data to

document the entire history of each mutual fund company in our data set. This not only allowed

us to ensure that each fund was assigned to the correct parent company in every year in the data

base, but it also allowed us to distinguish between firms that left the data due to acquisitions and

those that truly ceased their operations, an important distinction for survival analyses.

The mutual fund industry offers a number of advantages to probe the hypotheses discussed

above. First, the costs of opening a new mutual fund for an existing firm are relatively modest.

Easily-expanded distribution channels, the leveragability of brand and relationships, and the

relatively low variable costs of an additional fund manager, ensure that it will not only be

successful and thriving firms that have the opportunity to offer new products, should they choose

to do so. Second, this industry is comprised of firms utilizing very different strategies to succeed,

with some firms choosing to focus on only a single product offering (such as Edgemont Asset

Management which ran for 16 years only one fund, the up-to $6 billion Kaufman Fund), some

focusing on a narrow range of products (such as Flagship Financial with a focus on tax-exempt

state bond funds), and others offering a wide array of funds (such as Fidelity and Vanguard).

This indicates that there is no clear technological trajectory, with an obvious successful product

portfolio strategy for firms to follow.

13

Variables

Dependent variables

To measure firm performance, we use a commonly utilized variable in this industry – the

total cash inflows into all funds offered by a provider. This measure is a good indicator of

performance, since fund provider profits are, to a first approximation, increasing in the amount

of assets they manage (Chevalier and Ellison, 1997; Sirri and Tufano, 1998; Siggelkow, 2003).

Cash flows at the level of each fund can be estimated by the difference in fund size after

adjusting for appreciation (or depreciation) of the existing asset stock. We deflate these cash

flows to constant 1992 dollars and sum over all funds offered by a fund family to obtain variable

family flow.

For the survival analyses, we consider firms to have exited at the end of year t if year t is the

last full year in which business activity is reported. When we could find evidence that a provider

disappeared from our data because it was acquired by another firm (and its funds were rolled into

the acquiring firm’s portfolio), we labeled this firm as having exited by acquisition. Analysis of

our data showed that firms that were acquired had very different properties than firms that truly

ceased to exist. For instance, acquired firms were generally older than firms that failed (15 years

versus 10 years), had higher levels of investment performance (failing firms generally had an

investment return 0.5 standard deviations below the returns of continuing firms, while acquired

firms were not significantly different from continuing firms), and were larger (on average,

acquired firms were almost twice the size of failed firms). As a result, in the survival analyses we

only treat firms that cease operations as “failures.” Of the 885 firms that appear in our data set

between 1962 and 2002, 263 of them exit due to acquisition (30%) and 139 exit due to

organizational failure (16%).

14

Independent variables

To capture the degree to which a mutual fund provider shows explorative behavior, we

construct a measure that uses as its starting point a variant of the concentric diversification index

(Caves, et al., 1980), and is adjusted to take into account the degree of diversity that would occur

through random selection of product markets.1

The concentric diversification index is computed as: D = �� i ijij j dpp where pi is a measure

of activity for a particular fund provider in category i and dij is a measure of relatedness between

categories i and j. In our primary specification, pi represents the percent of all funds that a fund

provider offers. (See the “Robustness” section for the discussion of an alternative specification of

this variable.) For instance, if a provider offers nine funds, two of which are in category i, pi

would be 2/9 for that category. For all categories with no activity, pi is 0. Following Caves et al.

(1980), we set the distance measure between categories i and j, dij, to 0 for i = j; to 1 if i and j

belong to the same level 3; to 2 if i and j belong to the same level 2 (but not to the same level 3);

to 3 if i and j belong to the same level 1 (but not to the same level 2); and to 4 if i and j do not

belong to the same level 1. Thus, the diversification measure D has a minimum of 0, when all the

funds of a fund provider belong to the same category, and is increasing as the funds of a fund

provider are in ever-distant categories. While this measure provides an account of the

diversification of the firm, it does not capture two features that one might hope a measure of

exploration (or exploitation) would capture.

1 While there are documented concerns about the concentric diversification index (Robins and Wiersema, 2003), these concerns generally relate to trying to understand how “related” a portfolio of businesses is when one of the businesses is a dominant business (in this situation, the metric makes the portfolio look more related than it may truly be). As our use of the measure deals with the relatedness of product placement choices, we give each product placement choice the same weight, alleviating these concerns. One might also note that our measure of exploration addresses some of the additional concerns that Robins & Wiersema (2003) raise, including the aforementioned strong relationship between the concentric index and size.

15

First, there is an innate relationship between the size (the total number of funds) of a firm and

its diversification measure. For instance, since there are only three possibilities at level 1 (equity,

bond, and money market), a firm with four funds will necessarily have two funds that are at least

somewhat related to each other. As a result, even a firm whose choice of category in which to

open a new fund would be without any regard to its existing portfolio of funds would show some

level of relatedness. Conversely, as we saw in the hypothetical example above, additions of

related funds may make the firm appear more diversified, while the underlying fund-opening

activities are clearly of an exploitative nature.

Second, the measure of diversification does not take into account how many product

placement opportunities (i.e., categories) exist at a particular point in time. If the market space

were constant, this would not matter. However, in this industry (as in many other industries),

over time, new sub-markets opened up (Lounsbury and Rao, 2004). In 1962, at the beginning of

our study, only 28 (mostly equity) categories existed. Money market funds, for instance, did not

exist. By 2002, 83 sub-markets offered opportunities for exploration. As a result, it would be

natural to say that a firm that offered funds in, say, two equity categories in 1962 was more

explorative than a firm that offered the same funds in 2002, when many more product categories

were available.2

Our measure of exploration/exploitation takes these concerns into account. We simulate

1,000 firms that randomly open a given number of funds in a given year and compute the

average of these firms’ diversification indices. This allows us to construct a random benchmark

2 One should note that we treat the product space as exogenously given. For most firms in our sample this is a valid assumption. For the process of new category creation, see Lounsbury and Rao (2004).

16

for any family with a given number of funds in a given year.3 (By updating the benchmark

yearly, we take into account the number of fund categories that were available in any given

year.) Our measure of exploration is then the difference between the firm’s diversification index

and this benchmark. In the data, the mean of exploration is -0.29, indicating that (perhaps not

surprisingly) on average firms are more focused that a randomly diversifying firm. However,

exploration ranges from -3.15 to 0.54, showing a broad range of exploitative and explorative

behavior in the data.

By comparing a firm with N funds to its average benchmark firm with N funds, exploration

takes into account the innate relationship between diversification and firm size; as a matter of

fact, in the data the correlation between exploration and firm size is very small (0.01), while the

correlation between diversification and firm size is much larger (0.22) and highly significant (see

Table 1).

To gain further intuition for the differences in these measures, consider as example one firm

in our data set (AMR Investment Services) whose first 11 years of its history are mapped in

Figure 3. The stark difference in this case is that the diversification measure is generally

increasing while exploration is decreasing. Looking at the inset data panel, we can see that the

number of funds this firm offered increased about nine-fold from 4 to 35 during this 11 year

window, while the number of categories this family served only increased about three-fold from

4 in 1988 to 11 in 1998.4 Clearly, this is a firm that has (over this period) diversified its overall

portfolio, but has become more concentrated and focused than it was initially. The initial

placement of 4 funds in 4 categories is very broad, but the final placement of 35 funds in 11

3 A similar method of using a random benchmark to take into account a baseline level of activity has been used by Teece, et al. (1994) to construct a relatedness index for industries and by Ellison and Glaeser (1997) to construct a geographic concentration metric for industries. 4 These are the lowest-level categories, out of 76 possible in 1988 and 83 in 1998.

17

categories indicates a clear sign of an increase in focus. Thus, it would appear that the

diversification measure (which is increasing) is capturing the increase in diversification brought

on by the increase in size from 4 funds to 35 (and the placement of at least some of those funds

outside the initial 4 categories), while the exploration measure (which is decreasing) is capturing

the increased focus in the way in which the firm placed its new products over time.

Control variables

In our analyses, we include a range of control variables, at both the firm and the industry

level, that have been commonly employed in other studies of firm performance and firm survival

(Hannan and Freeman, 1989; Sorenson, 2000).

Each regression includes an aggregate measure of the quality of the products that a firm

offers. Product quality is measured by the performance of a family’s funds (since high fund

performance is enjoyed by the provider’s customers, the fund shareholders). Each fund’s

performance is measured by the difference of the fund’s return and the average return of all

funds in that fund’s category, divided by the standard deviation of all funds’ returns in this

category. The variable family product quality is then the weighted sum of the fund performances

of all funds within a family, where funds are weighted by their assets. Since the presumption is

that higher quality will subsequently increase cash flows, we have lagged this variable by one

year in all regressions.

Second, we control for the family size, by using the logarithm of the total assets the family

had under management, deflated to 1992 dollars.5 For all analyses, we use assets under

management at the end of the prior year.

5 For all analyses, we also tested a measure of size based on the log of the number of funds the family offered in a given year, and all results were similar.

18

Third, we include the age in years of each family, family age, from its first recorded offering

of a mutual fund product (which may be before 1962). Since the performance regressions use

year and firm dummies (see below), the age variable is not relevant for these regressions. But it

is included in the survival analyses.

Fourth, following the organizational ecology literature (Hannan and Freeman, 1989; Baum

and Singh, 1994), we include a measure of density. In particular, the variable density is the

average number of other families that offer funds in each of the categories in which the focal

family operates.

Lastly, given that families with one fund have by construction an exploration (and

diversification) value of zero, we include a dummy variable for all observations for which a

family had only one fund, dummy-one-fund, to distinguish this case from families with more

funds that have a level of exploration that equals that which would be expected by a randomly

diversifying firm.

The data are summarized in Table 1. Over the entire sample, the average family had $4.4

billion in assets under management and was 16 years old. The average family-year flow of new

investment to the firm’s funds was $312 million. As mentioned above, approximately 16% of the

firms studied failed in the window we covered, but the overall average chance of failure in any

given year was about 1%.

Data analysis procedures

All models testing performance hypotheses (H1 and H2) were performed using OLS

regression techniques with robust standard errors and allowing for non-independence of

observations within each family-panel. In these analyses, we also used firm fixed effects to

19

account for otherwise unobserved heterogeneity among the firms in the data set and dummy

variables for each year (1962-2002) to control for macroeconomic conditions.

To assess the relationship between exploration and firm survival described in H3 and H4, we

estimate the instantaneous hazard rate (Tuma and Hannan, 1984). The hazard rate is defined as

follows:

where pr(.) is the probability of failure in the period running from t to �t, conditional on being in

the market at time t. Our specification for time is based on the calendar year – we assume that all

firms in the market in the same year have the same baseline hazard in that year, with adjustments

made for firm age and other relevant covariates. We estimate h(t) using the Cox proportional

hazard model, which is characterized by the equation:

log h(t) = �(t) + �1x1 + �2x2 + …+ �kxk,

where � controls the shape of the baseline distribution over time. Though the raw data generally

showed an increasing hazard rate over time (indicating that the Weibull model might be

appropriate), there was significant variability (especially at the end of our sample) that made us

choose the Cox model. All tests indicate that the proportional hazards assumption that is core to

the Cox model has not been violated. For the sake of robustness, we also estimated the survival

models using a range of other event history analysis techniques. Our results were generally

robust across all models (see the Robustness section below). As our data set only contains

information to 2002, we were unable to perfectly determine which firms failed in 2002, so we

excluded the year 2002 from the survival analyses.6

6 As a test, we included the failures that we were able to determine for 2002 (along with all other data from 2002), and the results are qualitatively identical to those reported here.

pr (failure between t and t + �t | in market at t) �t h(t) = lim

�t�0

20

RESULTS

Impact of diversification and exploration on firm performance

In general, as shown by the results in the first column of Table 2, the control variables

performed as expected. The baseline model indicates that larger firms attract greater new

investment in any given year (firms that are one standard deviation bigger than average receive

an additional $371 million in new investment), and that families offering higher-performing

funds are able to attract more cash inflows (those with a performance one standard deviation

about the mean receive about $76 million in additional investment per year). Families competing

in densely occupied niches (those facing a greater average number of competitors in each of its

markets) experience significantly lower cash inflows, with a one standard deviation increase in

the average number of competitors a firm faces in its respective fund categories resulting in a

$356 million decrease in its annual net new investment. This may indicate a fixed pool of money

for any given fund category, and the more families are competing for that pool, the less (on

average) each individual family receives.

The dummy variable for firms with one fund indicates that these very small and focused

firms perform better than the model would predict based on asset size and performance alone.

(The average size of a one-fund family is about 1/8th the average size of multi-fund families.)

Comparing the overall predictions of our base model for an average 1-fund firm versus the

average 2-fund firm (including the effects of the other covariates, as one-fund firms tend to have,

for instance, smaller asset sizes and compete in more densely populated niches), yields a net

effect of about $200 million in additional revenue of one fund-families over two-fund families.

21

Model 2 includes the linear term of diversification and finds no significant effect. Model 3

includes the quadratic term of diversification as well. As predicted by H1, we find a curvi-linear

relationship between diversification and performance. The implied minimum point in the

relationship between diversification and performance is well within the range of our data (around

the 49th percentile of diversification), and indicates that both narrowly-focused firms (specialists)

and broadly-diversified firms (generalists) have the ability to outperform those firms that are

stuck in the middle.7, 8

Model 4 includes the measure of exploration instead of the measure of diversification.

Overall, we find a positive relationship between exploration and performance. Model 5 adds the

quadratic term of exploration. The results indicate, contrary to Hypothesis 2, a U-shaped

relationship rather than an inversely U-shaped relationship. (Even though both the linear and the

quadratic terms of exploration are positive, given that the range of exploration includes negative

and positive values, the observed marginal effect of exploration can be negative, tracing out an

entire U-shaped curve.) The minimum point of the relationship between exploration and

performance is within the range of our data, at the 7th percentile of exploration.9 Thus, looking at

the results of Model 5, we find that while more exploratory portfolios of products generally lead

to higher firm performance, there seems to be a small niche where extremely exploitative firms

can achieve higher levels of performance than those firms more towards the center of the

exploration continuum.

7 Re-estimating the relationship using only families with more than one fund yields a minimum point at the 29th percentile of diversification (in the distribution of diversification that excludes one-fund families). 8 One might also note that in Model 3 the coefficient on the one-fund-family dummy variable becomes insignificant. To test whether the non-linear term of diversification was unduly picking up a further non-linear relationship between family size and our performance variable, we estimated the model including a squared family size term as well. The results were qualitatively no different with respect to diversification and its square. 9 Re-estimating the relationship excluding families with one fund, yields a minimum point at the 9th percentile.

22

In sum, in this context of intra-industry exploration, we do not find an internal maximum

degree of exploration, i.e., no evidence for (what Kathleen Eisenhardt has at times jokingly

referred to as) the “Goldilocks” theory of exploration (a middle degree of exploration “that is just

right, not too much and not too little”). Rather we find a small area of beneficial extreme

exploitation, and a long range at which it is beneficial to increase exploration. One should note,

though, that given our intra-industry sample, very exploratory behavior (akin to unrelated

diversification) is not present. To assess whether decreasing returns to explorative behavior

appear in our data, we conducted a number of additional (unreported) analyses. Including higher-

order terms of exploration (to detect decreasing returns after the initial range of increasing

returns) and excluding families with one fund and families in the lowest 10 percentiles of

exploration, reveals an apparent internal maximum of exploration at the 94th percentile. At best,

one might call this weak evidence for a limit to the degree of beneficial exploration in our

industry context. In general, however, there appears to be a fairly robust positive relationship

between exploration and performance across a broad range of exploration in our data.

Both the effects of exploration and of diversification on firm performance are not only

statistically significant but also economically meaningful. A firm with a value of exploration in

the 75th percentile would attract $589 million more cash inflows in a given year than a firm with

the value of exploration that places it at the minimum of the quadratic. The benefits of extreme

exploitation are somewhat smaller. A focused firm with a value of exploration at the 3rd

percentile of our sample would realize $23 million in additional investment in any given year

than the “stuck in the middle” firm.

Likewise for diversification, the effects of straying from the minimum point are still

financially significant. For example, a firm with a level of diversification in the 25th percentile

23

would realize an additional $745 million in additional investment than a firm at the minimum

point. Likewise, a firm with a diversification measure in the 75th percentile would realize an

additional $529 million in incremental investment than a firm at the minimum point. Thus, the

diversification measure and the exploration measure would agree that the extremes of the

distribution are more attractive than the center.

Impact of diversification and exploration on failure

The first column of Table 3, reporting the results of the base line model for failure, including

only control variables, indicates that, as one would expect, an increase in overall firm size

decreases the firm’s likelihood of failure (a 50% decrease for firms one standard deviation above

the mean). Higher investment performance (product quality) does not have a significant impact

on survival. Unlike the traditional finding in the population ecology literature (Hannan and

Freeman, 1989), firm age does not significantly alter the life expectancy of the firm.

Interestingly, we also find that increases in the average number of competitors faced by a firm

decrease the firm’s chance of mortality (one standard deviation equals an 19% decrease in the

hazard). Since our measure of density looks at the actual competition faced by firms (rather than

the overall number of firms in the industry), this finding is in opposition to the traditional

findings that head-to-head competition from greater numbers of firms results in an increased

chance of mortality (Baum and Singh, 1994).10

Model 3 evaluates H3, which predicts an inversely U-shaped relationship between

diversification and failure, the mirror-image to that found between diversification and firm 10 Comparing densities of firms across different niches is difficult if the niches are heterogeneous in terms of overall size and attractiveness. Thus, a “better” measure of density would be a ratio of number of firms over the maximum carrying capacity of the market niche, which is difficult or impossible to obtain. Without that level of specifications, differing results can be expected. In this case, density may be capturing the inherent appeal of the market niche, so the number of firms competing in a market niche may be a signal of niche appeal, which may lead to increased survival chances.

24

performance earlier. The model indicates support for H3 and further shows that the effect of

diversification on mortality is indeed nearly the mirror image of the effect of diversification on

firm performance: for those levels of diversification for which the performance analyses indicate

high cash inflows, the survival analyses show lower levels of mortality. The maximum point for

failure lies at the 45th percentile of the data, with the failure rates decreasing in both tails, while

the minimum point for performance was at the 49th percentile. At the maximum point, the risk of

failure is 104% higher than it is at the 25th percentile, and 96% higher than at the 75th percentile.

In contrast to the performance regressions, for survival, Models 4 and 5 show marked

differences between the measure of diversification and the measure of exploration. While

diversification has the expected curvi-linear relationship, the variable exploration shows no

effect on mortality (neither linearly nor quadratically). Thus, we find no support for H4. Once

one adjusts for the random degree of diversification a firm would have with N funds, exploration

has no positive or negative effect on firm survival. Conscious exploration or focusing that goes

beyond random diversification appears to have no survival implications.

As discussed earlier, we collected data to distinguish between market exit due to acquisition

and due to failure. We estimated survival analyses with the risk of acquisition (vs. continuing) as

the relevant hazard (results available from the authors). In this specification, none of our control

or independent variables – save for density – could significantly explain the risks of being

acquired. Firms that operated in higher density areas of the product landscape were less likely to

be acquired, potentially reflecting the desire of potential acquirers to search out targets that offer

less common skill sets. Thus, in this industry setting, acquired firms look more like continuing

firms than failed firms. More generally, the differences in the empirical results highlight the

importance of differentiating between causes of exit.

25

ROBUSTNESS

We tested the robustness of our results along four dimensions. First, for the performance

models, to capture cross-family variation, and not only within-family variation, we dropped the

firm fixed effects. This did not change our core results in any way.

Second, since fund providers with one fund have, relative to the mean, high values of

exploration and low values of diversification, we checked the robustness of our results with

respect to dropping these observations (rather than controlling for them with a dummy variable).

Qualitatively, neither the performance nor the survival analyses were affected.

Third, for our survival analyses, we also used exponential and Weibull survival models. Both

produced results that were very similar to those reported here (in terms of signs and

significance), and switching the handling of ties between Efron, Breslow, and Exact methods

produced no significant changes. Given that firms were at risk of failing throughout the entire

year and that our data had specification only at the annual level, discrete-time event history

models also availed themselves (Allison, 1995). We tested both clustered and non-clustered

models, with both logit and complementary log-log link functions. All of these methods

produced very similar results to the Cox model specification. In the end, we decided to report the

Cox model results because (1) the model was clearly not exponential, as the hazard rate changed

over time, (2) ties were not very prevalent, so discrete-time models added complexity that did

not significantly change the outcomes, and (3) its ability to handle the inherent year-to-year

variability in the hazard rate made it the most appropriate.

Fourth, in our primary specification of the relatedness index, we considered the firm’s level

of activity to be based on the number of funds that it operated in a specific category. So if a 9-

26

fund firm had 2 funds in the same category, the weight on that category was 2/9. We had the

concern that the large number of state-funds, which we aggregated into one category, might lead

to some artifacts if this specification was used. To test this concern, we used an alternative

specification of the level of activity in a specific category, treating all categories equally

(regardless of the number of funds in that category). Thus, for a family with funds in 9

categories, each category would receive a weight of 1/9, and all other categories would receive a

0. We estimated all models with this alternative specification and found similar results.

DISCUSSION

This study furthers our understanding of the complex relationships between exploration,

diversification, performance, and survival in an intra-industry setting. On one hand, our work

finds that there is a “stuck in the middle” relationship between firm performance and exploration

or diversification – both narrow or wide product lines and broad exploration and high

exploitation can be financially successful strategies. While the existing literature on product

introductions has stressed the benefits of broader product portfolios, our research identifies

extreme focus as another successful strategy. The results demonstrate that in a setting where

there are not continuous pressures to introduce product innovations firms may be able to prosper

with a niche strategy. While the relationships between diversification and performance and

diversification and failure are mirror images of each other, we find that the relationship between

more exploratory product portfolios and firm performance is not mirrored in firm survival. Here

the distinction between diversification and exploration becomes critical. Exploration or

exploitation that goes beyond that which one would expect from a randomly diversifying firm

has no effect on firm survival.

27

As a scholar of management and strategy, one might certainly hope that purposefully driven

exploration has implications for a firm. At the same time, one can expect limits to managers’

abilities to foresee the consequences of actions due to bounded rationality – there are too many

factors at play for managers to properly take them into account, and thus the careful choices of

managers may be less than helpful. We do find some evidence that the degree of exploration (i.e.

conscious choice) matters in terms of firm performance, but we find no such relevance for firm

survival. For survival, having a broad array of products can help, but the selection of where to

place those products appears to be less relevant – a firm would be equally well off opening N

funds in randomly chosen categories. This finding thus identifies some boundaries to the effects

of intentional managerial actions.

This work draws an important distinction between diversification and exploration, where the

former is considered to be an estimation of the level of breadth of a firm’s product portfolio,

while the latter is a measure of the degree to which product placements could not be explained

by random choice. Our findings indicate that future research on the impacts of diversification

and exploration needs to be careful in how these concepts are operationalized, as there appear to

be real differences in their impacts on outcomes.

Our research also shed light on the question whether the theoretical work on exploration

(Robbins, 1952; March, 1991; Tushman and O'Reilly, 1996; Levinthal, 1997; Rivkin and

Siggelkow, 2003) that had pointed to the value of a balance of exploratory and exploitative

behavior applied to intra-industry settings as well. Our findings raise the possibility that the

traditional picture of an intermediate optimal degree of exploration does not fully capture the

phenomenon in an intra-industry environment. Not only do firms not appear to feel the negative

effects of over-exploration, there is also a small area of extreme exploitation that appears to lead

28

to increased performance. This indicates that the relationship between exploration and

performance may be more complex than originally thought.

More broadly, our findings point to potentially fruitful future research directions. If there is a

reason to believe that being more exploratory in produce placement choices can help increase the

performance of a firm, then does the inherent value of the product portfolio derive from the way

in which it was built? Do exploratory acquisitions benefit firms in the same way that exploratory

organic growth does? Likewise, is the eventual performance generated by high exploration

dependent on the path taken through product space that leads to this high explorative state, or is

it path independent?

29

Figu

re 1

: Tw

o di

ffer

ent p

rodu

ct p

lace

men

t str

ateg

ies

C

ateg

ory

1 C

ateg

ory

2 C

ateg

ory

3

Su

b-C

at 1

Su

b-C

at 2

Su

b-C

at 1

Su

b-C

at 2

Su

b-C

at 1

Su

b-C

at 2

T

ype

1 T

ype

2 T

ype

1 T

ype

2 T

ype

1 T

ype

2 T

ype

1 T

ype

2 T

ype

1 T

ype

2 T

ype

1 T

ype

2

Focu

sed

Firm

1

2 3

4 5

6 -

- -

- -

-

Bro

ad

Firm

1

- 4

- 2

- 5

- 3

- 6

-

T

he n

umbe

rs re

fer t

o th

e se

quen

ce in

whi

ch th

ese

two

firm

s in

trod

uced

thei

r pro

duct

s in

this

hyp

othe

tical

pro

duct

spa

ce.

30

Figu

re 2

: Com

pari

son

of e

xplo

ratio

n an

d di

vers

ifica

tion

for H

ypot

hetic

al F

irm

s

�����

�����

����

���

���

���

���

��

��

��������

��� �������

������������� � ���� ���������������

��������� � ���� ���������������

������������� � ���� �

��������� ������

��������� � ���� �

��������� ������

31

Figure 3: Comparison of exploration and diversification for AMR Investment Services

32

Table 1: Descriptive Statistics and Correlations

Variable Mean Std. Dev. flow failure quality size age density dummy divers explorefamily flow [millions $] 312.10 2,292.68 1.00 failure 0.01 0.12 (0.02) 1.00 family product quality (lagged) (0.01) 1.05 0.05 (0.06) 1.00 family size (lagged) [millions $] 4,406.09 22,964.20 0.50 (0.02) 0.04 1.00 family age 16.21 13.71 0.06 (0.05) 0.01 0.14 1.00 density 96.48 65.56 (0.05) 0.05 0.00 (0.08) (0.09) 1.00 dummy-one-fund 0.29 0.45 (0.08) 0.06 (0.08) (0.12) (0.17) 0.23 1.00 diversification (lagged) 1.51 1.25 0.15 (0.06) 0.08 0.22 0.25 (0.23) (0.76) 1.00 exploration (lagged) (0.29) 0.52 0.01 0.01 (0.02) 0.01 0.00 (0.01) 0.35 0.09 1.00

33

Tab

le 2

: The

Eff

ect o

f Exp

lora

tion

and

Div

ersi

fica

tion

on N

ew In

vest

men

t Rev

enue

s

fam

ily p

rodu

ct q

ualit

y (l

agge

d)72

.160

**72

.468

**69

.724

**66

.966

**68

.315

**(3

.30)

(3.2

5)(3

.19)

(3.1

9)(3

.22)

fam

ily s

ize

(lag

ged)

142.

438

**14

7.17

4**

123.

859

**14

8.33

0**

152.

359

**(3

.37)

(2.9

7)(2

.78)

(3.4

1)(3

.40)

dens

ity-5

.423

*-5

.580

*-4

.411

^-4

.839

*-5

.072

*(2

.30)

(2.1

3)(1

.92)

(2.2

1)(2

.23)

dum

my-

one-

fund

525.

990

*48

8.06

4**

22.0

1336

5.78

8*

372.

546

*(2

.35)

(2.9

0)(0

.28)

(1.9

8)(1

.99)

dive

rsifi

catio

n (l

agge

d)-3

7.57

1-9

55.1

93**

(0.4

4)(2

.87)

dive

rsifi

catio

n^2

(lag

ged)

306.

101

**(3

.15)

expl

orat

ion

(lag

ged)

380.

912

**83

2.74

6*

(2.7

5)(2

.56)

expl

orat

ion

^2 (l

agge

d)29

3.93

5*

(2.2

2)

firm

and

yea

r dum

mie

sin

clud

edin

clud

edin

clud

edin

clud

edin

clud

ed

N99

8099

8099

8099

8099

80R

^20.

276

0.27

60.

281

0.27

80.

279

NO

TE

: **

indi

cate

s si

gnifi

cant

at 0

.01

leve

l, *

indi

cate

s 0.

05 le

vel,

and

^ in

dica

tes

0.10

leve

lN

OT

E: t

-sta

tistic

s lis

ted

bene

ath

coef

ficie

nts

Mod

el 1

Mod

el 2

Mod

el 3

Mod

el 4

Mod

el 5

34

Table 3: The Effect of Exploration and Diversification on Firm Survival

family product quality (lagged) -0.055 -0.056 -0.058 ^ -0.055 -0.554(1.62) (1.63) (1.74) (1.61) (1.63)

family size (lagged) -0.266 ** -0.264 ** -0.254 ** -0.266 ** -0.264 **(7.20) (6.51) (6.16) (7.15) (6.91)

family age -0.011 -0.011 -0.009 -0.011 -0.011(1.31) (1.29) (1.10) (1.30) (1.30)

density -0.003 * -0.003 ** -0.003 ** -0.003 ** -0.003 **(2.73) (2.75) (2.86) (2.73) (2.76)

dummy-one-fund 0.399 ^ 0.375 0.788 * 0.452 ^ 0.448 *(1.91) (1.36) (2.28) (1.95) (1.96)

diversification (lagged) -0.016 0.963 *(0.13) (2.49)

diversification ^2 (lagged) -0.325 **(2.71)

exploration (lagged) -0.116 0.004(0.65) (0.01)

exploration ^2 (lagged) 0.071(0.27)

N 9902 9902 9902 9902 9902Log Likelihood -773.99 -773.98 -770.88 -773.78 -773.74Degrees of Freedom 6 7 8 7 8

NOTE: ** indicates significant at 0.01 level, * indicates 0.05 level, and ^ indicates 0.10 levelNOTE: z-statistics listed beneath coefficients

Model 5Model 1 Model 2 Model 3 Model 4

35

REFERENCES

Allison, Paul D.

1995 Survival Analysis Using the SAS System. Cary, NC: The SAS Institute.

Amihud, Yakov, and Baruch Lev

1981 "Risk Reduction as a Managerial Motive for Conglomerate Mergers." The Bell Journal of

Economics, 12: 605-617.

Baum, Joel A C, and Jitendra V Singh

1994 "Organizational niches and the dynamics of organizational mortality." The American

Journal of Sociology, 100: 346.

Berger, Philip G, and Eli Ofek

1995 "Diversification's effect on firm value." Journal of Financial Economics, 37: 39.

Bernheim, B. Douglas, and Michael D. Whinston

1990 "Multimarket contact and collusive behavior." The Rand Journal of Economics, 21: 1-26.

Caves, R. E., et al.

1980 Competition in the Open Economy. Cambridge, MA: Harvard University Press.

Chevalier, J., and G. Ellison

1997 "Risk taking by mutual funds as a response to incentives." Journal of Political Economy,

105: 1167-1200.

Collis, D.J., and C.A. Montgomery

1997 Corporate Strategy: Resources and the Scope of the Firm. New York: McGraw-Hill.

Cottrell, Tom, and Barrie R. Nault

2004 "Product variety and firm survival in the microcomputer software industry." Strategic

Management Journal, 25: 1005.

Daley, Lane, et al.

1997 "Corporate focus and value creation: Evidence from spinoffs." Journal of Financial

Economics, 45: 257.

36

Ellison, Glenn, and Edward L. Glaeser

1997 "Geographic concentration in U.S. manufacturing industries: A dartboard approach."

Journal of Political Economy, 105: 889-927.

Fleming, Lee, and Olav Sorenson

2004 "Science as a map in technological search." Strategic Management Journal, 25: 909.

Hannan, Michael T, and John Freeman

1989 Organizational Ecology. Cambridge, MA: Harvard University Press.

Kaufman, Carol Felker, and Paul M. Lane

1996 "A new look at one-stop shopping: A TIMES model approach to matching store hours and

shopper schedules." The Journal of Consumer Marketing, 13: 4-26.

Kekre, Sunder, and Kannan Srinivasan

1990 "Broader product line: A necessity to achieve success?" Management Science, 36: 1216-

1231.

Koza, Mitchell P, and Arie Y Lewin

1998 "The co-evolution of strategic alliances." Organization Science, 9: 255.

Levinthal, Daniel A

1997 "Adaptation on rugged landscapes." Management Science, 43: 934-950.

Lounsbury, Michael, and Hayagreeva Rao

2004 "Sources of durability and change in market classifications: A study of the reconstitution of

product categories in the American mutual fund industry, 1944–1985." Social Forces, 82: 969-

999.

March, James G.

1991 "Exploration and exploitation in organizational learning." Organization Science, 2: 71-87.

Montgomery, Cynthia A., and Birger Wernerfelt

1988 "Diversification, ricardian rents, and Tobin's q." The Rand Journal of Economics, 19: 623.

37

Morck, R., et al.

1990 "Do managerial objectives drive bad acquisitions?" Journal of Finance, 45: 31-48.

Nelson, Richard R, and Sidney G Winter

1982 An Evolutionary Theory of the Economic Change. Cambridge, MA: Harvard University

Press.

Perloff, Jeffrey M., and Steven C. Salop

1985 "Equilibrium with product differentiation." The Review of Economic Studies, 52: 107-120.

Rivkin, Jan W, and Nicolaj Siggelkow

2003 "Balancing search and stability: Interdependencies among elements of organizational

design." Management Science, 49: 290-311.

Robbins, H

1952 "Some aspects of the sequential design of experiments." Bulletin of the American

Mathematical Society, 58: 527-535.

Robins, James A., and Margarethe F. Wiersema

2003 "The measurement of corporate portfolio strategy: Analysis of the content validity of

related diversification indexes." Strategic Management Journal, 24: 39-59.

Rosenkopf, Lori, and Atul Nerkar

2001 "Beyond local search: Boundary-spanning, exploration, and impact in the optical disk

industry." Strategic Management Journal, 22: 287.

Rothaermel, Frank T, and David L Deeds

2004 "Exploration and exploitation alliances in biotechnology: A system of new product

development." Strategic Management Journal, 25: 201.

Rumelt, Richard P.

1986 Strategy, Structure and Economic Performance. Boston: Harvard Business School Press.

Schmalensee, Richard

38

1978 "Entry deterrence in the ready-to-eat breakfast cereal industry." The Bell Journal of

Economics, 9: 305-327.

Sheth, Jagdish N.

1983 "Emerging Trends for the Retailing Industry." Journal of Retailing, 59.

Siggelkow, Nicolaj

2003 "Why focus? A study of intra-industry focus effects." The Journal of Industrial Economics,

51: 121.

Singh, Jitendra V., et al.

1986 "Organizational change and organizational mortality." Administrative Science Quarterly,

31: 587.

Sirri, E., and P. Tufano

1998 "Costly search and mutual fund flows." Journal of Finance, 53: 1589-1622.

Sorenson, Olav

2000 "Letting the market work for you: An evolutionary perspective on product strategy."

Strategic Management Journal, 21: 577.

Stern, Ithai, and Andrew D Henderson

2004 "Within-business diversification in technology-intensive industries." Strategic Management

Journal, 25: 487.

Teece, David J, et al.

1994 "Understanding corporate coherence: Theory and evidence." Journal of Economic Behavior

& Organization, 23: 1-30.

Tuma, N.B, and Michael T Hannan

1984 Social Dynamics: Models and Methods. New York: Academic Press.

Tushman, Michael L, and Charles A III O'Reilly

1996 "Ambidextrous organizations: Managing evolutionary and revolutionary change."

California Management Review, 38: 8-30.

39

Vassolo, Roberto S., et al.

2004 "Non-additivity in portfolios of exploration activities: a real options-based analysis of

equity alliances in biotechnology." Strategic Management Journal, 25: 1045.

40

Appendix 1: Classification scheme of mutual fund categories

1 – Equity 1 – General Domestic 1 – Growth

1 – Growth/Capital Appreciation (GMC) 110101 2 – Growth (GRO) 110102 3 – Principal Return Funds (EPR) 110103 2 – Aggressive Growth 1 – Aggressive Growth (AGG) 110201 2 – Small-Company Growth (SCG) 110202 3 – Convertible Bonds (CVR) 110203

3 – Income and Growth 1 – Income-Growth (ING) 110301 2 – Growth and Income (GRI) 110302 4 – Option Income (OPI) 110401 5 – Corporate Preferred (CPF) 110501 2 – Special Domestic

1 – Gold (GLD) 120101 2 – Natural Resources (NTR) 120201 3 – Real Estate (RLE) 120301 4 – Environment Sector (ENV) 120401 5 – Financial Sector (FIN) 120501 6 – Health Sector (HLT) 120601 7 – Miscellaneous (SEC) 120701 8 – Technology Sector (TEC) 120801 9 – Utility Sector (UTI) 120901

3 – Hybrid Domestic 1 – Balanced (BAL) 130101 2 – Flexible (FLX) 130102 3 – Corporate Income Mixed (IMX) 130103

4 – International 1 – Asian 1 – Chinese Equity (ECH) 140101 2 – Japanese Equity (EJP, JPN) 140102 3 – Pacific Equity (PAC) 140104 [only one fund] 4 – Pacific Basin Equity (including Japan) (EPC) 140104 5 – Pacific Basin Equity (excluding Japan) (EPX) 140105 2 – Europe 1 – European Equity (ERP) 140201 3 – Emerging 1 – International Developing Markets (EID) 140301 2 – Latin American Equity (ELT) 140302 4 – Canada 1 – Canada Equity (ECN) 140401 5 – Global 1 – Global Growth (EGG, EIG) 140501 2 – Global Small-Cap (EGS, EIS) 140502 3 – Global Equity Sector (EGX, GLE) 140503 4 – Single Country Equity (ESC) 140504 5 – Global Total Return (EGT, EIT) 140505

41

6 – Flexible Global (FLG) 140506

2 – Bonds 1 – Domestic 1 – Corporate Bonds, high quality 1 – General Corporate Bonds (CGN) 210101 2 – High-quality Corporate Bonds (CHQ) 210102 3 – Corporate Intermediate Maturity (CIM) 210103 4 – Corporate Short Maturity (CSM) 210104 2 – Corporate Bonds, low quality 1 – High-yield Corporate Bonds (CHY) 210201 2 – Corporate Medium Quality (CMQ) 210202 3 – Corporate Short-term Intermediate Maturity (CSI) 210203 3 – Government Bonds

1 – Government General (GGN) – 210301 2 – Government Intermediate Maturity (GIM) – 210302 3 – Government Adjusted Rate Mortgage (GMA) - 210303 4 – Government Mortgage-backed (GMB) - 210304 5 – Government Short Maturity (GSM) - 210305

4 – Municipal Bonds 1 – Municipal General (MGN) 210401 2 – Municipal High-Yield (MHY) 210402 3 – Municipal Intermediate Maturity (MIM) 210403 4 – Municipal Insured (MIS) 210404 5 – Municipal Short Maturity (MSM) 210405

5 – State Tax-Free Bonds 1 – Tax-Free Bonds 210500

6– State Intermediate Maturity Tax-Free bonds 1 – Intermediate Maturity Tax-Free bonds 210600

7– State Short Maturity Tax-Free Bonds 1 – Short Maturity Tax-Free Bonds 210700

8– Corporate Prime Rate Funds (CPR) 210801

2 – International 1 – Global North American Bonds 1 – Global North American Bonds (BGA) 220101 2 – Global Emerging

1 – Global Emerging Market Bonds (BGE) 220201 3 – Global Government

1 – Global Government Bonds (BGG) 220301 2 – Global Bond General (BGN, GBG) 220302 4 – Global Short-term Bonds

1 – Global Bond Short-Term (BGS, GBS) 220401 5 – Single Country Bonds (BGC) 220501

3 – Money Market 1 – Domestic 1 – Common

1 – Government and Agency Money Market (SUA) 310101

42

2 – Government Money Market (SUT) 310102 3 – Money Market Prime (SPR) 310103 4 – Money Market Prime and Euro (SPE) 310104 5 – Money Market Prime, Euro and Yankee (SPY) 310105

2 – State Tax-Free 1 – Tax-Free Money Market 310200

3 – Institutional 1 – Institutional Government and Agency (SIA) 31301 2 – Institutional Government Money Market (SIT) 31302 3 – Institutional Money Market Prime (SIP) 31303

4 – Institutional Money Market Prime and Euro (SIE) 31304 5 – Institutional Money Market Prime, Euro and Yankee (SIY) 31305 6 – Tax-Free Money Market – Institution (TFI) 31306

4 – Bank (6) 1 – Bank, Government and Agency Money Market (SBA) 310401 2 – Bank money market prime (SBP) 310402 3 – Bank government money market (SBT) 310403 4– Tax-Free Money Market – Bank Managed (TBG) 310404 5 – Bank Money Market Prime and Euro (SBE) 310405 6 – Bank Money Market Prime (Euro and Yankee) (SBY) 310406

2 – International (1) 1 – Foreign Currency (1)

1 – Foreign Currency (SCU) 320101