Market Convergence & Firm Strategies: Towards a systematic analysis
Johannes M. Pennings
Professor, Management Department, Wharton
Pennings@wharton.upenn.edu
Phanish Puranam
Doctoral Candidate,Management Department, Wharton
puranam@management.wharton.upenn.edu
The Wharton School of the University of Pennsylvania
2000 Steinberg-Dietrich Hall
Philadelphia, PA 19104-6207
Phone:215-898-7755 or 898-1228,fax:215-898-0401
January/February, 2000
Keywords: convergence, digital imaging, network methods
Paper presented at the
Organization Science Winter Conference
Keystone, CO
Market Convergence & Firm Strategies: Towards a systematic analysis
Abstract
This paper seeks to explore the phenomenon of market convergence- a process by which markets become
increasingly similar over time. The impact of this process is to erode market boundaries, and create strategic
challenges for firms by causing them to face up to new sets of technologies, needs and customers. In this
research we try to develop a framework to understand the drivers of convergence and the role that firm
strategies play in convergence processes.
Firms are prone to fall into a competency trap as they accumulate intangible assets, which have conferred a
competitive advantage. Some strategies for learning will aggravate the consequences of path dependence,
while other might diminish or even suspend that dependence. We review acquisitions and strategic alliances,
which differ in their irreversibility, flexibility and level of commitment to new and remote technologies and
customers. We also review drivers of convergence and include swift triggers such as deregulation and delayed
ones such as socio-economic developments and technological breakthroughs.
To complement our theory building, we present preliminary results from the analysis of a particular industrydigital imaging, which is being created out of the convergence of photography, computer hardware & software
and electronics, in an attempt to show how the convergence phenomenon may be measured and investigated
using network methodologies. We explore the growing centrality and density in a network of four-digit SIC
industries in order to isolate the speed, and progression of technological convergence.
Market Convergence & Firm Strategies: Towards a systematic analysis
Introduction
In this paper we undertake an attempt to systematically map out the convergence in
technologies and markets. After outlining a framework we provide some empirical analysis
to illustrate some issues. We seek to answer three questions: What is the convergence
phenomenon, why does convergence occur, and how do firms cope with its implications
strategically? We believe that the convergence phenomenon should take center stage in the
research and theory on technological change, innovation and corporate strategy, as it
constitutes a major impetus for growth, development and renewal, while also causing much
upheaval, market disequilibria and firm mortality. We explore the nature of convergence
and differentiate between the supply and demand side of the market that undergoes
convergence.
Convergence can be viewed as the unbundling and repacking of technologies or
their commercial application into product-market concepts. Convergence between
previously disjointed markets can be viewed as the erosion of boundaries that define and
isolate industry-specific knowledge. That knowledge resides in the firms who collectively
combine their firm-specific knowledge into industry specific knowledge. We observe that
process on both the supply side—for example bundling or fusion of technologies, as well as
on the demand side when previously heterogeneous sets of customers become similar, or
there is a pull towards satisfying multiple needs in a single transaction. We propose a
taxonomy of convergence processes that embraces both producer and consumer issues.
Next we attempt to identify a range of drivers that trigger the onset of convergence.
Sometimes, we witness a sudden surge ofconvergence—for example after the deregulation
of the telecommunication and financial services sectors. In other cases, the trigger has
delayed effects—for example socio-economic developments such as globalization, or
homogenization of customer segments due to changing demographics. In still other cases
the driver might be a technological innovation whose speed may depend on underlying
technological oportunity.
We also examine the strategic implications of convergence.
The dominant
paradigm in strategic management and innovation has centered around the resource based
view of the firm (Penrose, 1959), wichh has a major implication that firms need to be
“coherent” (Teece et al., 1994) in their accumulation of intangible assets. They face limits
in the quest for diversification. That very accumulation renders firms path dependent. As a
contrast to the coherence imperative, convergence might wreak havoc with the firm and its
coherent bundle of intangible assets. When convergence takes hold, firms are forced to
abandon a strict adherence to coherence, and seek to suspend the competency trap that
comes with path dependence. We thus observe a tension between coherence and
adaptiveness to convergence. In their corporate development activities, firms may choose
between internal development, acquisitions and strategic alliances to suspend the path
dependence and branch out to other markets that undergo convergence.
Studying convergence empirically presents several problems. For analytic purposes,
we need to arrive at a demarcation of market boundaries before we can study any changes in
them over time, and this constitutes a fairly serious challenge to any theory of market
convergence. At the heart of the problem is the fact that stable market boundaries are
definable only in an equilibrium situation, whereas convergence is by nature a
disequilibriating process. Hence, our approach to the empirical study of market convergence
will necessarily involve some assumptions about prior period equilibrium, and the validity
of certain industry classification schemes, in particular the Standard Industrial
Classification scheme. An additional problem is the measurement of the relationships
between industries, and how they change over time. In the empirical part of this paper, we
present preliminary results from the analysis of a particular industry- digital imaging-,
which is being created out of the convergence of photography, computer hardware &
software and electronics. Using data on the incidence of inter-organizational relationships
such as joint ventures, alliances, and acquisitions, and well established measures of network
structure, the changing patterns of linkages between industries is used to demonstrate
convergence, and suggest possible means to measure the phenomenon. We do not present
any formal tests of hypotheses in this paper, but restrict ourselves to developing a
systematic approach to understanding and describing an important phenomena which is
actively shaping today’s economic landscape.
Market convergence: conceptualization and definitional clarity
In this section, we present a basic, definitional framework, which will aid in the subsequent
analysis of the market convergence phenomenon. We conceptualize a market to be
composed of five sets of elements, i.e, firms that use technologies to create products that
satisfy needs of customers. This enumeration constitutes little more than an elaboration of
the conventional economic definition of markets as the aggregate of buyers and sellers who
come together for the determination of the price for a product (Pindyck and Rubenfeld
1998); we have merely made explicit two features of the market which are usually
unobservable, i.e, needs and technologies.
To make the discussion clearer, we present a simple representation, which might be
called a “convergence map”. (See Diagram 1) This map simply indicates that at any given
point in time, the configuration of firms (Fi), technologies (Tj), products (Pk), customer
needs (Nl) and customers themselves (Cm) may be represented as a series of mappings (oneto many, many-to-one or many to many).
There exists an underlying mapping from a given technology to the needs set, which
is exogenous to the decisions by firms and customers (as shown by the box around the
technology-needs mapping) i.e. each technology satisfies a certain need or set of needs.1
Typically only the set of firms, products and customers is directly observable. Technologies
and needs are only indirectly measurable. In particular, we conceptualize products as being
embodiments of technology-need mappings. We believe that neither firms nor customers
can treat the others’ needs or technologies respectively as exogenous; firms can, and do
manipulate the motivation of customers through advertising (Pennings and Kim, 2000).
Similarly, by signaling unfulfilled needs consumers can influence the direction of
technological development (Schmookler 1966). For conceptual clarity however, our
discussion of convergence phenomena will treat endogenous changes in customer needs (or
firm technologies) as different from those influenced by firms (or customers) and will focus
on the former.
The convergence map is a "snapshot" representation at a single point in time. Over
time, the mappings are likely to change. In fact, it is particular kinds of change in these
mappings, which we identify as market convergence. Given that the mapping from
technologies to needs is invariant, changes only occur when the elements in the set of firms
or customers change, the mapping from firms to technologies change or the mapping from
1
Our conceptualization builds on notions of local search in solution spaces employed by organizational
scholars in the behavioral tradition (Cyert and March 1963, Nelson & Winter 1982) We assume that
technologies exist "out there" which do not reside in any firm but are gradually discovered by firms. Also,
some technologies are proximate to each other while others are distant.
customers to needs change. Our representation allows us to offer a clear discussion of
various cases of market convergence. Broadly speaking, market convergence may occur on
the demand side or on the supply side.
Demand side
One archetype of demand side convergence can be inferred from the growing similarity of
needs across groups of consumers. This is a phenomenon, well known in the marketing
literature, which is sometimes termed the convergence of markets. Levitt (1983) for
instance in an award winning article argued that globalization of world markets could be
inferred from local, national markets becoming homogeneous across the world, with
customers across these markets evolving to a uniform set of needs. Socio-economic
developments towards global diffusion of transportation, telecommunication, FDI and
foreign trade have contributed to the convergence of needs of customers across the world.
In contrast, de-regulation produces a second archetype of demand side convergence,
which may be termed “product bundling.” If the avoidable transaction costs of customers
are sufficiently high, there may be a pull from the customer’s side for “one stop shopping,”
i.e. a desire to obtain in a single transaction a product which satisfies multiple needs. The
financial services and telecommunication industries illustrate this convergence. De–
regulation permits the bundling of formerly disparate products such as commercial,
consumer and investment banking, or local and long distance and wireless telephony, or
even power, gas and internet connectivity. While de-regulation was the driver, what is
critical in these industries is customers recognizing a set of distinct, though related set of
needs as capable of being satisfied in a single transaction, thus reducing transaction costs.
Hence the location of this kind of convergence on the demand side. An important aspect of
convergence brought about by de-regulation is its high speed; de-regulation removes
artificial barriers between industries almost overnight, as opposed to gradual convergence
across customer groups which is driven socio-economic developments as illustrated by the
above mentioned globalization of markets. These examples represent respectively the
convergence of needs –either across consumers, or within consumers. In the former case,
different consumers acquire the same needs, whereas in the latter, the same set of
consumers see distinct needs as closely related, which they can satisfy in a single
transaction.
Supply side
One archetype of convergence on the supply side occurs when new technologies are
discovered which map onto needs already being satisfied by existing technologies. This
implies that the needs satisfied by different technological capabilities begin to overlap to a
greater extent (note the analogy with convergence of needs across customers) and creates a
situation where one technology supplants another by offering the same benefits, eventually,
perhaps at lower costs. Examples of this sort of convergence abound in the popular press;
specifically in computers and communication industries, digital imaging and photography
industries, as well as some sectors of biotech and pharmaceuticals. Underlying many of the
examples in the IT industry is the presence of an integrative technology platform – digital
computing- which is enabling activities in other industries (such as photography and
communications ) to be performed more efficiently.
The preceding archetype of convergence should be distinguished from another that
has been termed “technology bundling“ or “fusion” (Teece 1996). Fusion involves
recombination of existing technologies in new and innovative configurations. In other
words, firms must see some possible economies of scope/synergy effects when bundling
various technologies. Examples would include the coming together of technologies such as
optics and electronics, and biology and instrumentation to create opto-electronics and bioengineering respectively.
The two archetypes of convergence on the supply side represent the convergence of
technologies – in the first case , different technologies become similar in terms of the needs
they can satisfy, and in the second, different technologies come together to create new
functionality or improve efficiency of existing products. While the drivers of both kinds of
technological convergence are technological innovations, they are likely to differ in terms
of whether they occur in the same firm or in different firms.2 The speed of convergence in
either of these cases is largely a function of technological popportunity. (See the research
based on the Yale & Carnegie studies of appropriability and technological opportunity, for
instance Klevorick et al 1992).All the instances of convergence, whether on the demand
side or the supply side share the same common attribute- formerly distinct markets become
similar, eroding the boundaries between them.
Before concluding this brief overview, we must point out that our taxonomy of
convergence phenomena has centered around the initial causes and patterns of convergence;
in reality, convergence is a dynamic and often a self re-inforcing phenomenon . Thus, as
convergence progresses, we may witness multiple kinds of convergence, which may also
occur at increasing rates. Computer hardware and software is almost always sold today as a
bundle. Innovations in each realm are increasingly targeted to exploit innovations in the
other- improvements in quality, breadth and speed of computer hardware enable more
powerful software to be designed, and improvements in software programming enable
2
For instance, technology fusion requires close co-ordination- this would imply the sort of incentive
alignment and low powered incentives offered by a hierarchy. On the other hand, the creation of technological
innovation which cannibalizes existent technologies, is unlikely to be taken up by a firm internally, and is
more likely to be introduced in a competing firm, often an entrepreneurial entrant from another industry.
optimal utilization of hardware, and hence design improvements. Biotechnology, which was
initially seen as being in competition with conventional pharmacology because of its
abilities to satisfy the same broad class of needs, has evolved into an integral part of the
pharmaceutical industry. Every large pharmaceutical firm has its host of biotechnology
partners, and its propriatary R&D and product developments are geared towards managing
these relationships. In retrospect, the final outcome in these computer and biotech examples
looks like technology fusion, though neither of the two convergence cases originated as
technology fusion-the first started out as a case of product bundling (demand side
convergence) and the second as technology substitution ( supply side convergence).
Summarizing, we have identified convergence on both the demand and supply side,
and have spelled out some of the drivers that trigger the process. On the demand side we
mentioned socio-econmic developments and deregulation. On the supply side we referred
to technological innovation as the prime driver. In the next section we complete the
elements of a framework and spell out some strategic implications regarding convergence
on both sides of the market.
Firm strategies and market convergence: A framework
The typology presented in the last section was meant to be useful in identifying the various
archetypes of convergence and their possible antecedents. We now turn to a discussion on
the strategic implications of various kinds of convergence. In the first part of this section,
we develop an axiomatic framework to classify the archetypes of market convergence and
study the role that firms play in driving and responding to them. Apart from distinguishing
convergence as occurring on the demand side or on the supply side, another approach is to
distinguish between substitute and complement
effects from the perspective of firms
(Khanna & Greenstein 1997).
On the demand side, the convergence of needs across consumers implies that
different groups of consumers become substitutable with each other, as they grow similar in
terms of needs. The convergence of related products into a single product bundle implies
that there are complementarities between multiple needs- the value to serving them together
is higher than serving them individually. On the supply side, convergence across
technologies which causes one technology to offer the benefits of the other illustrates
technological substitution.The convergence between technologies caused by technology
fusion or bundling exemplify complementarities. These ideas are summarised in Diagram
2.
All convergence processes have the impact of eroding boundaries between
industries, and thereby posing stratgeic challenges to firms, causing them to face new
technologies, consumers, and needs. To reiterate, the common aspect among multiple forms
of convergence is to degrade or render obsolete current organizational capabilities.
However, the ability of firms to refashion them to new demands is inhibited by path
dependence (Teece Pisano and Shuen 1997). Thus, convergence and path dependence
produce a unique tension: firms should abide by the coherence imperative (Teece et al
1994) and “stick to the knitting,” while convergence induces them to diversify by venturing
into new and uncharted markets. It is this dichotomy between coherence and convergence
that must be resolved through corporate strategy. Faced with this dilemma, firms attempt to
extend their capabilities from external sources through a menu of corporate development
activities. These activities include alliances, joint ventures, R&D partnerships and mergers
and acquisitions (M&A). (Chesbrough & Teece 1996, Nagarajan and Mitchell 1998, Pisano
1990). Due to the uniqe aspects of each convergence form, such as its drivers, rate of
convergence, or substitute/complement effects, we expect that different strategic responses
might occur in different convergence scenarios.
Depending on the extent and speed
of adaptation required, firms favor either
internal development, technology “grafting” through mergers and acquisitions (Puranam
1999), or relational development through alliances and joint ventures. Some guidance on
likely choices is to be found in the literature. Kogut and Zander (1992) argue, for instance,
that firms will diversify into (technologically) related areas through internal development
and into remote areas through joint ventures. They outsource activities which are complex
and difficult to learn, or will buy other firms when under survival pressure. Chesbrough and
Teece (1996) and Monteverde (1995) suggest that when joint technological development
activities, or technology transfer requires close collaboration between parties, a hierarchical
governance structure with well-aligned, low powered incentives is optimal.
Recall that convergence on the demand side is usually induced through growing
similarity of needs among diverse customers. In this form of convergence with concomitant
substitutability of consumer groups, firms must modify their current product offering to
keep pace with the trend. They can accomplish this through internal development or
interorganizational relationships, depending on how "far" the new product configuration is
from their current configuration. Such practices are widely documented for multinatiuonal
corporations pondering the alternative of wholly owned subsidiaries versus joint ventures
when entering new markets with their existing product line (e.g., Barkema, Douma and
Pennings, 1996).
For demand side convergence involving complementarities in product bundling,
possible responses include product/service bundling, enhancement of
corporate scope
through diversification and inter-firm liaisons. First, intermediary firms may arise which
aggregate the services of other firms e.g., mortgage service providers typically bundle
products (credit ratings, mortgage servicing, loans and other financing services) for
customers in a manner which is useful to them. Second, when real economies of scope
prevail firms extend the realm of proprietary knowledge through internal development or
M&A. The recent entry of Bell Atlantic into long distance telephony illustrates this strategic
move. The telecommunication infrastructure can be exploited by adding new services to
local telephony and other services already provided through that infrastructure. Finally,
absent such economies, firms resort to inter-organizational relationships including alliances,
cross selling agreements etc. The recently announced alliance between Microsoft and Nokia
shows how inter-firm arrangements rather than outright acquisition or internal development
allow two firms to merge product offerings—in this case email software and cellular based
Internet connectivity. While insufficient real economies of scope to joint operation exist
which would justify merging these firms, there are clear complementarities to providing a
bundle of these products to consumers.
In the case of technology convergence of the substitution variety, firms face a
daunting scenario. Technology substitution represents the classic instance of a competence
destroying change. Incumbents are unlikely to be responsive to the technological changes
which allow entrants to intrude into their product space. If on the other hand, incumbent
firms become aware of this threat at an early stage, they are likely to form alliances with
firms which embody the new, substitute technology. As the pace of the convergence picks
up, survival pressures render acquisition mandatory (Kogut & Zander 1992). Entrants, on
the other hand may mostly grow through internal development and perhaps occassionally
through alliances, if the distribution channels or other key complementary asets are
controlled by the incumbents. Biotechnology firms often commercialize their knowledge by
contracting with pharmaceutical firms to penetrate a market, or increasingly with Clinical
Research Organizations (CRO’s) such as Quintiles.
Finally, in the case of technology bundling due to complementarities, we would
expect the rise of technology consortia and other strategic inter-organizational relationships
to jointly "search" for the new, combined technology. However, as Chesbrough and Teece
(1996) , and Monteverde (1995) suggest, if differences between the underlying knowledge
bases of different technologies is substantial, close collaboration become essential, and
therefore a hierarchical form of governance (acquisition or internal development) is best
suited to the organization of this transaction. Further, if the nature of research needed for
combining technology is basic rather than applied, we should expect it to be carried out by,
or under the charge of govermental agencies and univeristies. For example, NIST’s AMP
(National Institute of Standards and Technology’s Advanced Technology Program) is a
good example of both these effects; the program is geared to the funding of risky forms of
technology bundling with the objective of commercializing them eventually. Part of NIST’s
contribution is simply the funding of risky, basic research which is not commercially viable
for firms, but the creation of a new entity (consortia that have representatives from NIST)
which approximates a hierarchical governance mode
also serves to align incentives and
imrove co-operation.The implication from these observations is that different patterns of
inter-firm arrangements are bound to surface under these various forms of convergence.
In the rest of this paper, we apply this framework to the digital imaging arena which
has arisen from the convergence of photography, computing, and electronics industries.
Originating in 1984 with the release of the first commercial digital camera by Eikonix, the
arena has matured over the years with participants entering from computing, electronics,
and photography. The digital imaging arena has also seen considerable M&A and alliance
activity , beginning with Kodak’s acquisition of Eikonix in 1984 to the recent series of
technology consortia between computing and photography firms. We therefore view this
arena as having great potential for describing and explaining convergence.
Digital Imaging: A brief history and an overview of the technology
The essence of digital imaging is that it involves devices that take pictures and
develop them using electrons instead of film and then transmit, store, and process these
images electronically, as if they were files of data. NASA developed digital imaging
technology in the early 1970’s for its space program; this technology was closely tied to
computer technology, and as costs of computer processing fell, the technology began
diffusing into other areas. From the realm of consumer electronics, the development of
video cameras had an impact on the way initial digital cameras were configured. Video
technology had already shown that it was possible to dispense with film, though that
industry remained rooted in analog technology till the late eighties. Prior to 1990, the usage
of digital photography was largely restricted to a few scientific (medicine and satellite
imaging) and commercial (publishing and real estate marketing) applications. The primary
advantages of this technology were the ability to manipulate and edit pictures on computers
and the ease and speed of development, storage, recall and transmission. With decreasing
costs and increasing functionality in many of the component technologies of digital
imaging, particularly semiconductors, computer hardware and software, it has since been
making steady inroads into conventional film based imaging, as well as spawning new
products and services.
Historically, digital imaging is an arena which contains participants from multiple
industries. One group came from traditional film based imaging (eg. Kodak), to whom
digital imaging represents a competence destroying innovation. Another group came from
the consumer electronics industry (eg. Panasonic), and typically attempted to leverage their
experience with video cameras into digital imaging, particularly in the early stages of digital
imaging. Yet another group of firms originated in the graphic arts and printing industry (eg.
Scitex) which had pioneered the use of electronic scanning. Finally, there were entrants
from the computer hardware, software and semiconductor industries (eg. Intel, Hewlett
Packard) as digital cameras began to be accepted as computer peripherals.Digital imaging
today draws on technological competencies from the semiconductor and electronics
industries, computer hardware and software industries, and conventional film based
imaging industries. An enumeration of the components of a standard digital camera
illustrates this.
The basic image capture technology is based on the CCD (Charge Capture Device)
sensor, which serves the function of converting light energy into a digital data file. The
CCD technology has remained virtually dominant till recently, when CMOS (Combined
Metal oxide Semiconductor) based technology has begun to replace it; sensors using CMOS
sensors are about ten times as energy efficient as CCD’s, and cost substantially less. The
earliest versions of digital cameras did not have any storage device, thus severely
constraining the portability of the instrument as it meant attachment via cables to a
computer. Today, there are two major competing formats for the storage of digital photo
files; removable PCMIA cards and micro drives. These are removable media, which
effectively function like a roll of film. The file format in which the digital images are
transferred to a computer, and then further undergo manipulation is another critical aspect
of the digital camera industry. Today, there are competing alternatives available for the
format in which digital imaging files can be stored, as well as for the software used to
manipulate and use these data image files. Finally, there is a microprocessor chip, which
controls the operation of the camera. Its key metric is speed, and size. (In addition, most
present day digital cameras have an LCD display, and a lithium battery to meet the power
requirements.) Related components of the architecture are printers, computers and other
visual display devices.
An empirical investigation of convergence in the digital imaging arena
The convergence of the computing, electronics, and photography industries to create
digital imaging is an instance of convergence steming from the supply side. A series of
technological innovations in semiconductors, electronics, computer hardware and software
have cumulatively provided a viable alternative to film based imaging, and one which has
already surpassed it on several parameters. In doing so, the boundaries between these
industries have become weakened. There is an interesting asymmetry between the
perspectives of firms in electronics and computing, and those in photography, on
convergence in digital imaging. For firms in the photography industries, digital imaging
represents a technology substituting convergence prcess, and is potentially capable of being
competence destroying, but for firms in the computing and electronics, digital imaging
represents an oportunity to exploit technology complementarities between their competence
in electronics and digital computing, and conventional photography.
In this section, we attempt to provide a description of the process of convergence
between the industries associated with digital imaging, and demonstrate how network
methodologies may be useful for this purpose. In keeping with our earlier discussion, we
rely on the observed corporate development activities of firms to understand the linkages
between industries.
3
To identify the industries associated with digital imaging, we started
out with a list of all companies, which have ever introduced a digital camera to the market 4.
We assume that the SIC codes which describe the parent industries of the firms which
introduced digital cameras was an accurate classification system prior to the emergence of
digital imaging technology. We uncovered 76 firms, which have announced the product
launch of a digital camera, the first documented launch being in 1984. There were 20
principal SIC codes and these broadly fell into four sub-groups of firms- computing
(hardware, software, design and peripherals), consumer & other electronics (household
audio and video equipment, electrical appliances, semiconductors), photography (optical
instruments, equipment and supplies) and miscellaneous (news services, holding
companies, dolls and stuffed toys etc.). We could not obtain the primary SIC codes for 9
firms, presumably because they were startups. This group of 20 SIC codes is henceforth
referred to as the “digital group”. [Tables 1 & 2]
Trends in inter-firms activity
We obtained data on corporate development activity (i.e. alliances, including joint
ventures, and acquisitions) by all firms in these 20 SIC industries from SDC Platinum.
Figure 1 & 2 highlight some of the important trends in these activities. Numerically,
alliancing activity within the “digital group” rose to a peak of about 30% of all alliances
involving firms in the digital group in 1989 and again in 1995. Separating these within
“digital group” inter-firm links into within SIC and across SIC transactions, it appears that
The data for this exploratory study come from Security Data Corporation (SDC), which include among other things
annual data on mergers and acquisitions and strategic alliances, and S&P’s COMPUSTAT.
4 This list was compiled from issues of the Future Image Report as well as Lexis-Nexis.
whereas up to 1994 the within SIC dominated the across SIC transactions, since 1994, the
difference between these types of links have been sharply reduced. This implies that since
1994, alliances with firms in other “host” industries than the “home” industry have been as
important as alliances within the home industry by firms in the digital group, which is one
indicator of convergence among these industries. The alliances within the digital group as a
percentage of all alliances shows a cyclical pattern. Perhaps these trends represent
alternating patterns of exploration and exploitation, as firms within the digital group
alternately build relationships with firms outside and inside the group. (Figure 1)
Acquisitions within the group display a relatively straightforward upward trend.
While initially, the number of acquisitions outside the group was higher than acquisitions
within the group, by the late eighties this trend was reversed. Till the early nineties,
acquisitions within the group were almost equally divided into within-SIC and across-SIC
transactions, but subsequently, within-SIC transactions have dominated other categories.
(Figure 2)
These trends are broadly consistent with a period of convergence and consolidation.
The differences in the trends seen in alliances and acquisitions may be attributable to the
fundamental differences between alliancing and acquisition activity- Kogut and Zander
(1992) suggest that alliancing and joint venturing activity is more likely for exploratory
development. Thus the farther the domain of knowledge from the current one, the more
likely that alliances are used instead of acquisitions in expansion efforts. Acquisitions are
more drastic, irreversible investments. Alliances, in contrast are more prone to occur across
SIC industries, entailing diversification that is inherently more risky and unknown, and
therefore more suitable in view of their flexibility. Alliances amount certainly to less of a
commitment of corporate resources, and resemble therefore the notion of ”real options.”
(Kogut and Kulatilaka 1994).
Inter-industry networking
While examining the transactions of the “digital group” as a whole offers some
preliminary, interesting insights, we will demonstrate how network methodologies can be
used to deepen our understanding of the convergence process, and provide metrics for the
same. Examining only aggregate transactions does not allow us to distinguish, for instance,
between convergence among the SIC codes which make up a sub-group, or across SIC
codes which are unrelated to photography, and hence imaging. Using the data on number of
M&A and alliances between SIC codes, valued adjacency matrices were constructed for
each year between 1984 and 1998. Figures 11 & 12 offer a pictorial representation of M&A
and alliances over time, allowing one to visualize the increasing density and changes in
patterns of inter-industry linkages. For ease in analysis, we break down the “digital group”
into the four subgroups mentioned earlier (Pg 17) –computing, electronics, photography and
miscellaneous. Apart from providing a visual representation of linkages between industries,
network methodologies provide several algorithms for measuring connectivity. In
particular, we used four network parameters to describe the relationship between industries
in the digital group over time. We use these parameters to describe two phenomena – the
overall convergence within this group of industries, as well as the specific pattern of interlinkages between the major sub-groups (i.e. computing, electronics and photography).
While using network parameters, one must keep in mind the important caveat that
unlike in social network analysis, industries are non-sentient actors and the linkages studied
here are not of the nature of long chains of communication flows- we merely borrow some
existing algorithms to analyze the patterns of linkages between nodes in a graph. The four
parameters for uncovering the digital convergence process are as follows:
Clique membership: a clique is a sub-graph, which is fully interconnected. Thus a set of
industries which are reciprocally tied to each other through alliance or acquisitions links
represents a clique. For each year, we first detect all cliques with membership greater than
3. Then, we calculate the percentage of these cliques, which are composed of members
from different sub-groups. Finally, we calculate the percentage of across sub-group cliques,
which included firms from the photography sub-group. This measure of across sub-group
clique formation, studied over time, gives an estimate of the extent and rate of convergence.
Component membership: Two vertices x and y are in the same strong component if there
is a path connecting x to y and a path connecting y to x. Thus membership in a component
implies reciprocal paths between all the members. By calculating the percentage of
industries in the “digital group” which belong to the same component, and observing this
measure over time, we are able to provide an estimate of how much (and how fast)
convergence within this group of industries is taking place.
Degree Centrality: The number of vertices adjacent to a given vertex in a symmetric graph
is the degree of that vertex. For a given binary network with vertices v1.... vn and maximum
centrality Cmax, the network centralization measure is (Cmax - C (vi)) divided by the
maximum value possible, where C (vi) is the centrality of vertex vi. This measure is used to
estimate the extent to which individual industries are connected to the digital group, as well
as the overall interconnectivity of the network.
Density: The density of a binary network is the total number of ties divided by the total
number of possible ties. For a valued network it is the total of all values divided by the
number of possible ties. We partition the adjacency matrix based on sub-group
membership, and then study the trends in density within, and across sub-groups. This allows
us to understand the specific patterns of inter-linkages within the “digital group” which
cause convergence.
It should be noted that we are implicitly using the notion of cohesion (ties) to
measure the strength of linkages between industries. In social network analysis, two
network methodologies (cohesion and structural equivalence) compete for legitimacy in
displaying the patterns of contact among actors (Burt, 1992; Strang and Tuma, 1993).
Structural equivalence shows the relative similarity of each pair of industries in being
linked with the remaining members of the industry set. Cohesion reveals the actual direct
(and indirect) “contacts between each and every industry. Intuitively, cohesion is the most
compelling method for our problem, as one would be hard pressed to view industries as
competing for partnering with other industries. Rather, it seems more appropriate to search
for growing proximity between the various industries through the incidence of alliances or
acquisitions among its constitutive firms. We should identify cohesive blocks of industries,
rather than structurally equivalent blocks. In other words, changes in adjacency rather than
competition underpin the notion of convergence.
Figures 3-10 present the various network parameters that we calculated on the
adjacency matrices. Figures 3 and 4 depict three measures of the network of digital group
firms over time. The first is component membership. Nodes in a graph belong to the same
component if there are reciprocal paths between them. The metric in Figures 3 and 4
captures the percentage of industries in the digital group, which belong to the same
component, over time. Density captures the extent of interconnections in the network.
Centrality indicates the average number of connections that a node in the network has. As
can be seen there is a clear increasing trend in the acquisitions network for all three,
indicating that the industries in the group are becoming more closely interconnected over
time, and also that each individual industry is more tightly linked to the network as a whole.
The alliance network shows some cyclicality and after peaking in 1995, all three measures
show a decline. This is consistent with our findings from the analysis of alliances to within
group and outside the group industries. We interpret these findings in line with our earlier
remarks about exploration and exploitation.
The next four graphs continue to show network trends signaling digital convergence,
but we have singled out the traditional imaging industries, i.e., photography, as providing a
distinct benchmark. Using M&A and alliances, we ask how well the photography industries
are connected with the digital group as a whole. We also ask how likely these photography
industries are included in cliques, thus shedding light on their relative marginality or
centrality, against a backdrop of the digital group as a whole. Figures 5 & 6 are density
measures, disaggregated by sub group. Thus, after partitioning the adjacency matrix by sub
group, we calculated the density within and across groups. For both acquisitions and
alliances, the computer sub-group showed the higher internal density across all years.
Further, for both acquisitions and alliances, the density of ties between the computer and
photography group are higher than the density of ties between the photography and
electronics group. Thus, we conclude that the photography industry is converging towards
the computing industry more than it is towards the consumer and other electronics
industries. Finally, for acquisitions only, the convergence between computers and
photography is stronger than that between computers and electronics, whereas the converse
is true for alliances. This might be in part because the relationship between the computing
and electronics industries is a vertical one, and the observed alliances might have been
motivated by supply requirements.
Figures 7 and 8 represent the average degree centrality of members in the
photography sub group, compared to the centrality of the entire group, on average. As the
adjacency matrices for acquisitions are not symmetric, we calculated separate in centrality
(acquiring) and out centrality (acquired by) measures. Findings from these measures show
that while the photography industries might be involved in the convergence of the industries
in the digital group, they do not form most central industries. The computing industries
occupy that positions, and are clearly the most important contributors to the convergence in
this group.
Figures 9 & 10 represent the percentage of cliques in the group, which are composed
of members from different subgroups (cross cliques), and also the percentage of cross
cliques, which have a member from the photography industry. Consistent with previous
results, the alliances network is almost completely dominated by cross cliques, whereas the
acquisitions network shows a gradual increase of cross cliques over time. For acquisitions,
the proportion of these cross cliques which have an industry from the photography subgroup reaches its peak of bout 50% in 1989 and then levels off. For alliances, this
proportion starts at very high levels, and after fluctuating between 1985 and 1990, grows
steadily till 1993, and then levels off. One explanation for the high initial values might be
that prior to the first release of a digital camera in 1984, there might already have been a
gradual buildup in cross industry alliancing activity, which we could not observe because of
data limitations. (Our data on alliances is left censored beyond 1984). Finally, our results
are suggestive of the sub groups that figure more prominently in the convergence process.
Cumulatively, the network measures make it possible to measure the extent, as well
as the rates of convergence across industries. In particular, they help us to understand which
sub-groups within the digital group are more tightly connected than others, and also the
sub-groups which are most active in the convergence process.
Discussion
At the onset of this paper we suggested that the convergence phenomenon could be
uncovered through inter-firm activities, including myriad forms of strategic alliances as
well as mergers and acquisitions. Through these activities firms are able to escape their path
dependence which derives from their quest to accumulate a coherent set of distinct
competencies (Teece et al., 1994) such that they can extend their boundaries into unrelated
areas (Kogut and Zander, 1992).
We sought to complement the question of what is convergence, with the questions
regarding the how (e.g., firm strategy) and the why (i.e., drivers) of convergence. The
phenomenon requires us to consider the firm, its industry and a set of potentially
complementary and overlapping industries. There is a profound tension between the firm,
and its need to be coherent with its industry that might increasingly blend with other
industries and therefore call the coherence imperative into question. We have stressed that
convergence can be decomposed into different classes, most notably supply (technology and
firms) versus demand (needs and customers). Furthermore, we imply these classes of
convergence evoke different types of corporate strategic responses with the supply
convergence inviting more enduring and irreversible commitments such as joint ventures,
corporate ventures and internal developments (or if too delayed swifter efforts, but with a
cost such as acquisitions). If the convergence is demand driven, strategic responses might
be more flexible and reversible, for example collaborative marketing and distribution
agreements. Three drivers were spelled out: (de) regulation, socio-economic developments
and technological innovations that destroy current competencies or create new ones. To
illustrate our arguments convergence was mapped in the digital imaging arena. Taking
information on alliances and mergers and acquisitions (M&A), network methods were
employed to uncover the growing overlap in a number of relevant imaging industries during
the period 1984-1999. We showed how strategic alliances, and acquisitions contribute to
the blurring of pre-existing boundaries and the rise of new blocks of industries, and also
how their incidence can be used to measure the extent and rate of the convergence process.
Firm versus Industry
The convergence process on the supply side, uncovered in this empirical study, does not
inform us about heterogenity of responses by firms in an industry. We have assumed that
technological innovations constitute the drivers of the “digital convergence”(Yoffie, 1997),
but we have remained agnostic about the motivation of firms to participate in this process
Neither have we shed any light on the relative prominence of firms in triggering or shaping
convergence as a technological trajectory. For example, how do firms acquire absorptive
capacity in surviving the onslaught that is associated with technological convergence? Will
internal ventures endow them with the capacity to adapt on the emerging landscape that we
call digital imaging. Is convergence endogenous to the set of industries that undergo
convergence? If so, should we attribute crucial disequilibrating effects to the conduct of
some firms? Indeed, what role do we attribute to firms in choreographing the convergence
process, or alternatively, which firms are central in mediating the observed convergence?
From our empirical analysis it should not be surprising that firms with social capital
should be viewed as pivotal in triggering or shaping convergence. Extending firm
boundaries by linking up with other firms and their competencies as opposed to building
new competencies internally, and by branching out to firms with distant technologies
requires a substantial stock of “relational competencies” (Kale 1997, Khanna 1998). A
track record of forging strategic alliances or succesful acquisitions is testimony to such
competencies. We would therefore surmise that the cumulative level of alliance activity or
acquisition activity predicts a firm’s prominence in the convergence process.
While we constructed networks of industries based on their firms’ inter-firm
activities and observe changing patterns of density, we should also recognize
compelmentary data and methods for mapping the process. Alternative methods for tracking
convergence include the creation of networks, based on patent citation patterns within and
across disciplines. The convergence thus construed is more basic and need not culminate in
market overlap or shifting boundaries (e.g. Almeida, 1997). Unlike patent citations, interfirm alliances and acquisitions entail commitment of (intangible and tangible) assets and
reveal a rearrangement of the corporate landscape. Thus, convergence derived from
corporate development actions might be a lagging convergence indicator. Patent citations
reveal growing overlap in technological fields, might be a leading convergence indicator. In
any event, attempot to use patent citations might render our exploration more robust, and
should, at a minimum, further help us in traingulating the measurement of convergence.
Similarly, we should obtain insights about a firm’s technological know–how in
shaping digital convergence. Digital imaging is a unique area as it embodies the
combinative skills of hardware, software and photography capabilities. We would surmise
here that absorptive capacity matters; the firms which have been able to reach out to digital
imaging relevant partners, or who have been active in expanding their home grown bundles
of expertise should be better positioned in shaping the technological trajectory that embeds
their expertise. A possible way for evaluating a firm’s stock of relevant technological skills
is an inventory of its patents in digitally oriented products or technologies. We did not track
the firms’ range of intellectual proprieties in this study but it seems plausible that these
bundles should define a firm’s readiness to participate in the center rather than in the
periphery of the convergence process.
Finally, we believe there is value to studying the tension imposed by convergence on
the coherence of firms. Firms are motivated to, and often advised to stay “close to the
knitting” and to forego unrelated diversification. We believe that a major impetus to future
research and theory is the abandonment of a simplistic inquiry into the relatedness problem
of strategic diversification and instead a proposal to identify a balance between firm
specialization and despecialization. The concern with competency traps, real options, and
organizational learning are testimony to the need to search for an optimal tradeoff among
various firms strategies.
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Firm Strategies and Market Convergence
Demand Side
Supply Side
Substitution
Complementarity
Different technological capabilities
become similar in the sense that they
can satisfy the same set of needs.
Different technologies are brought
together to create new kinds of
technology
Speed: Depends on tech.opportunity
and appropriability
Speed: Depends on tech.opportunity
and appropriability
Eg.Computing, communication, and
imaging, biotechnology & pharmacology
Eg.Optoelectronics, bioinstrumentation
The needs of different
consumer sets becomes
similar.
Different (but related)
needs are met by bundling
products together.
Speed:Gradual
Speed:Rapid if led by de-regulation
Globalization of markets,homogenization Eg. Commercial, consumer and
of demographic segments.
investment banking, hardware & software
Diagram 2
Table 1
List of primary SIC codes of all firms which have launched a digital camera
SIC
2835
3555
3571
3572
3577
3579
3651
3674
3827
3841
3861
3942
5043
5045
5064
5065
6719
7372
7373
7383
Group
MISC
MISC
COMP
COMP
COMP
ELEC
ELEC
ELEC
PHOTO
MISC
PHOTO
MISC
PHOTO
COMP
ELEC
ELEC
MISC
COMP
COMP
MISC
Industry
IN VITRO, IN VIVO DIAGNOSTICS
PRINTING TRADES MACHY, EQUIP
ELECTRONIC COMPUTERS
COMPUTER STORAGE DEVICES
COMPUTER PERIPHERAL EQ, NEC
OFFICE MACHINES, NEC
HOUSEHOLD AUDIO & VIDEO EQ
SEMICONDUCTOR, RELATED DEVICE
OPTICAL INSTRUMENTS & LENSES
SURGICAL, MED INSTR, APPARATUS
PHOTOGRAPHIC EQUIP & SUPPL
DOLLS AND STUFFED TOYS
PHOTOGRAPHIC EQUIPMENT AND SUPPLIES-WHLSL
COMPUTERS, PERIPHERALS, AND SOFTWARE-WHLSL
ELECTRICAL APPLIANCES TV AND RADIO-WHLSL
ELECTRONIC PARTS & EQUIPMENT-WHLSL
OFFICES OF HOLDING COMPANIES
PREPACKAGED SOFTWARE
COMPUTERS INTEGRATED SYSTEM DESIGN
NEWS SYNDICATES
Table 2
Entry of firms into digital camera market
Number of entries as of Jan 1, 1999
Of which startups (founded after Jan 1, 1985)
From home industries
COMP
PHOTO
ELEC
MISC
Unknown
Computers, Software and peripherals
Photographic equipment & supplies
Consumer & other electronics
Miscellaneous
76
26
28
17
12
10
9
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Figure 1
Trends in M&A activities by firms in
the "Digital Group"
600
Series1
500
Ttransactions
Series2
400
Series3
300
200
100
0
1978
1980
1982
1984
1986
1988
Y ear
1990
1992
1994
1996
1998
2000
Series 1
Number of acquisitions outside the “digital group” by firms in the “digital group”
Series 2
Number of acquisitions within the digital group (but not within the same SIC
code) by firms in the “digital group.
Series 3
Number of acquisitions within the same SIC code by firms in the “digital group.
Figure 2
Trends in Alliance/JV activities by firms
in the "Digital Group"
0.25
Series1
0.2
Series2
0.15
0.1
0.05
0
1984
Series 1
Series 2
1986
1988
1990
Year
1992
1994
1996
1998
2000
Number of alliances within the digital group (but not within the same SIC code)
normalized by total number of alliances made by firms in the “digital group”.
Number of alliances within the same SIC code by firms in the digital group normalized
by total number of alliances made by firms in the “digital group”.
Figure 3
%age
M&A activity:Network parameters
80
60
40
20
0
1980
1985 1990
1995 2000CompMemb
Density
Centrality
Years
Figure 4
%age
200
JV/Alliance activity :Network
parameters
150
100
CompMemb
Density
Centrality
50
0
1980
CompMemb
Density
Centrality
1985
1990
1995
Year
2000
Percentage of industries (in the digital group), which are members of a single
strong component
The average density of the network. (Scaled)
The degree centrality of the network.
Figure 5
Density within and across subgroups
misc
Acquisitions
2.5
comp
elec
2
1.5
1
0.5
photo
comp-photo
elec-photo
comp-elec
Density
0
1980
-0.5
1985
1990
1995
2000
Figure 6
Density within and across sub-groups
Alliances
5
comp
4
elec
photo
3
comp-photo
2
elec-photo
comp-elec
1
Density
0
1980
Key
Misc
Comp
Electronics
Photo
Comp-photo
Elec-Photo
Comp-Elec
Density
1985
1990
1995
Miscellaneous sub group
Computing sub group
Electronics sub- group
Photography sub group
A sub group composed of computing and photography
A sub groiup composed of electronics and photogrpahy
A sub group compsed of computing and electronics
Density of the entire digital group
2000
Figure 7
Degree Centrality of Photo Subgroup
Acquisitions
12
10
Out
8
In
6
Mean
4
2
0
1980
-2
1985
1990
1995
2000
Figure 8
Degree centrality of Photo Subgroup
Alliances
35
30
25
20
15
10
5
0
1980
PhotoCentrality
Centrality
(mean)
1985
1990
1995
2000
Key
Out
In
Mean
PhotoCentrality
Out-centrality of the photography sub group
In centrality of the photography sub group
Mean centrality of the entire digital group
Centrality of the photography sub-group
Note: because the acquisitions adjacency matrix is not symmetric, both in and out centrality are
calculated for them. The adjacency matrices for alliances are symmetric.
Figure 9
%crosscliques
120.00
Cliques within Digital Group
Acquisitions
100.00%photocrosscliqu
es
80.00
60.00
40.00
20.00
0.00
1980
-20.00
1985
1990
1995
2000
Figure 10
%crosscliquesCliques within digital group
120%photocrosscliq
Alliances
100ues
80
60
40
20
0
1980
1985
1990
1995
2000
Key
%crosscliqes
The percentage of the total number of cliques which involved members from
different sub-groups.
%photocrosscliques The percentage of %crosscliques which involved a memebr from the
photography sub-group.