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Marketing Management in
MNC Subsidiairies:
An Archetypal Analysis of
Heterogeneity in Strategy
and Organization
_______________
David MIDGLEY
Sunil VENAIK
2012/72/MKT
Marketing Management in MNC Subsidiaries:
An Archetypal Analysis of Heterogeneity in Strategy
and Organization
David Midgley*
Sunil Venaik**
August 2012
* Professor of Marketing at INSEAD, Boulevard de Constance 77305 Fontainebleau Cedex.
Email: [email protected]
** Senior Lecturer in Strategy at The University of Queensland, Brisbane St Lucia, QLD 4072,
Australia. Email: [email protected]
A Working Paper is the author‘s intellectual property. It is intended as a means to promote research to
interested readers. Its content should not be copied or hosted on any server without written permission
from [email protected]
Find more INSEAD papers at http://www.insead.edu/facultyresearch/research/search_papers.cfm
2
ABSTRACT
The relative degree of control and standardization in the ways multinational corporations
operate across the globe has been a central question in international business research since
the inception of the discipline. Normatively, is it better to control from the center or should
local subsidiaries be allowed to go their own way? Or is it more appropriate to globally
standardize certain elements of operations but allow local adaptation on others? Moreover,
due to the dynamism and diversity of the business environments in which MNCs operate, they
constantly seek better strategies and decision-making structures to balance the evolving global
and local pressures on their subsidiaries. Consequently there is considerable heterogeneity in
the strategies and structures employed by individual corporations as they adapt to the specific
circumstances in which they find themselves. And paradoxically, although the literature
examines in depth the moderating influence of various pressures on strategy and structure, it
does not describe the underlying empirical heterogeneity of these strategies and structures in
any detail. In this paper, we use a relatively new statistical method—archetypal analysis—and
detailed measures of the marketing mix to describe the heterogeneity of subsidiary marketing
strategies and decision-making structures. Our preliminary results show this heterogeneity to
be more complex than recognized by the literature.
Keywords: Archetypal analysis, MNC subsidiaries, adaptation, innovation, autonomy,
networking
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INTRODUCTION
With increasing globalization of the world economy and the spread of multinational
corporations worldwide, there is continuing interest in examining the strategy and
organization of these multinational corporations (hereafter MNCs). MNCs account for a
quarter of global GDP, and their foreign affiliates ―more than one-tenth of global GDP and
one-third of world exports‖ (UNCTAD, 2011: 1). Understanding their strategy and
organization is therefore of central interest to scholars of international business.
The earlier literature on MNCs mainly focused on the strategies and decisions taken in
the headquarters of the parent company (Buzzell, 1968; Levitt, 1983). However, with growing
size and significance of MNC subsidiaries, scholarly interest shifted to understanding
strategies and decision-making at the level of the local subsidiaries. Prahalad and Doz‘s
(1987) seminal work on global integration and local responsiveness documents the diverse
pressures confronted by MNCs as they expand globally and the strategies they pursue in
response to these environmental pressures. Although Prahalad and Doz proposed a broad
framework, many of the early debates on how MNCs should respond to these pressures were
more limited in scope. For example, should firms standardize or adapt their strategies as they
expand globally? For another example, should firms centralize their decision-making in the
corporate headquarters or provide autonomy to local subsidiaries for subsidiary-level
decisions? Bartlett and Ghoshal (1989) extended these discussions by adding considerations
of subsidiary learning and innovation. In doing so, they highlight the need for internal
networking and coordinated decision-making across the MNC organization. Bartlett and
Ghoshal argue such coordination is necessary to support inter-unit learning and accelerate
innovation in MNCs. Continuing in this tradition, Devinney, Midgley and Venaik (2000)
extend the integration-responsiveness framework to include the transactional pressures on the
MNC‘s value chain and formalize the key role managerial beliefs play in the strategic choices
4
made by such organizations. And, in an empirical paper, Venaik, Midgley and Devinney
(2005) show there are dual paths to better MNC performance. One path is through subsidiary
decision-making autonomy, which encourages innovation, and the other path is through
networking, which encourages inter-unit learning.
Notwithstanding the broadening of discussion in the international business literature, the
international marketing literature continues to focus on the simple standardization-adaptation
debate (Takeuchi & Porter, 1986; Lages, Jap & Griffith, 2008). While recognizing the
importance of this debate, and the deeper understanding that has grown from it, we believe
there is a need for international marketing scholars to embrace the organizational issue of
autonomy versus centralization. In addition, the critical significance of local market
innovation for MNC subsidiary competitiveness and the need for global networking to
discuss, decide, share and compare marketing best practices across the MNC also needs
recognition in the international marketing literature.
Equally, we believe the international business strategy literature could benefit by
adopting the detailed perspective seen in international marketing studies. For example, a
marketing strategy can be broken down at least to the level of the 4Ps (price, product,
promotion and place) if not to a finer level of detail. Zou and Cavusgil (2002) develop a
model of global marketing strategy that includes the four Ps plus marketing concentration and
coordination, and global market participation. Given the diversity of the global business
environment and the wide range of industries in which MNCs compete, the devil is surely in
the specific details of adaptation, innovation, autonomy and networking. Which aspects of
strategy are adapted, which are standardized? What is the country or global focus of
innovation for an MNC? Over which decisions are subsidiaries given autonomy and over
which are they not? And what are the priority topics in networking and coordination
meetings? All in all, we believe there is a need for more breadth in marketing studies and
5
more detail in business strategy studies. But more importantly, our overall understanding of
MNCs would greatly benefit from more detailed data on adaptation, innovation, autonomy,
and networking.
More detailed data would require scholars to break down each of these four areas into a
number of subordinate components, develop measures of these components and collect the
necessary supporting data on MNC subsidiaries. However, having more detailed data on
these critical areas of MNC operation admittedly makes analysis of these data more
challenging. We will have a larger number of components to analyze and we believe these
may display a greater degree of heterogeneity across MNC subsidiaries than the literature
discusses. For example, if we break down marketing strategy into 4Ps this may uncover
subsidiaries where product and place are standardized but pricing and promotion are adapted.
Indeed, as we increase the number of subordinate constructs more combinations potentially
exist—be these of adaptation or standardization, autonomy or centralization, etc. Hence,
there is consequent need to identify common patterns in these data and thus represent MNC
heterogeneity in a concise, understandable and meaningful way. Existing techniques such as
cross-tabulation or cluster analysis may not meet this need. For this reason, we also believe
we need new techniques to analyze these data in ways that shed deeper insight on how MNCs
respond to pressures in their business environment.
The objectives of our paper are thus twofold. First, to demonstrate the benefits of a
more detailed view on strategy and organization. We do this by defining the appropriate
subordinate components through a ‗7Ps‘ framework. Essentially this framework builds on
marketing‘s traditional 4Ps but adds components for product positioning, MNC marketing
policies and personnel. This results in a coherent framework that can be applied across the
four areas of adaptation, innovation, autonomy, and networking. We then develop and
validate multi-item measures for these 7Ps and collect the supporting data from a sample of
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229 MNC subsidiaries. We should note that while we do this in the context of marketing our
approach could be readily extended to other areas of MNC operations. Second, we analyze
these data by archetypal analysis (hereafter AA). As will be discussed subsequently, AA
provides an advance over existing techniques because the resulting common patterns—MNC
subsidiary archetypes—have a clear definition. As a consequence these archetypes are a
powerful summary of the heterogeneity of MNC subsidiary operations. AA also uses a
mixture model. This allows insightful comparisons between MNCs that are clearly
associated with one single archetype and those with a strategy or organization that is a
mixture of two or more archetypes. A major contribution of our work is to demonstrate that
these mixed strategies and organizations are more prevalent than the literature suggests. A
second contribution is to demonstrate that the strategies and organizations the archetypes
themselves represent are more heterogeneous than those discussed in the literature. Finally,
we also believe our application of AA is novel within the international business literature.
The paper is organized as follows. The next two sections provide an overview of the
four key subsidiary level issues in international marketing, namely, the strategies of local
adaptation and local innovation, and the question of decision-making through local autonomy
and internal networking. In the section that follows these, we discuss the 7Ps framework,
introduce the basic ideas behind AA and present our research hypotheses. This is followed by
another section in which we discuss the methodology we use for our study—including
component measures, survey design, sampling and the application of AA to our data. We
conclude our paper by presenting and discussing our results and outlining the potential
contributions they make to the international business literature. The paper also includes a
technical appendix that provides deeper explanation of AA and demonstrates the convergent
and discriminant validity of our measures and archetypes.
7
MNC SUBSIDIARY STRATEGY
A large body of scholarly work in international business, and especially work focusing on
international marketing, examines the impact of the twin pressures of global integration and
local responsiveness along the standardization-adaptation dimension (see Nasir & Altinbasak,
2009 for a recent review). To what extent are the MNC‘s marketing activities standardized
across its various subsidiary operations? Or to what extent do local subsidiaries adapt
elements of the 4Ps to their local market? Further, with growing global and local competition
in the local markets, MNCs are under increasing pressure to use their subsidiaries as sources
of innovations that can be deployed around the globe (Nobel & Birkinshaw, 1998). Is it
possible to leverage an innovation developed by one subsidiary across the other markets in
which the MNC operates? These two strategic imperatives---local adaptation and local
innovation—are central to thinking about MNC strategy and organization. We view local
adaptation as reflecting the degree to which price, product, promotion and place (and their
constituent elements) are modified to suit the requirements of the local market. And we view
local innovation as the degree to which the subsidiary seeks new ideas for improving elements
of its price, product, promotion and place. We now briefly review each of these two areas.
Local Adaptation
The importance of environmental and institutional forces on firm strategy and decision-
making is acknowledged widely in both the international business literature (e.g., Porter,
1990; Venaik, Midgley & Devinney, 2004) and the organization theory literature (e.g.,
Lawrence & Lorsch, 1967; Sundaram & Black, 1992). In particular, MNCs are confronted
with diverse and often conflicting environmental pressures as they expand their activities
around the globe. These pressures are often broadly referred to as the pressures of global
integration (GI) and local responsiveness (LR) (Prahalad & Doz, 1987). The GI pressures
force firms to take an integrated approach to their global activities—that is, to coordinate their
8
business units and strategies to attain maximum efficiency and competitive advantage. These
pressures might lead to responses such as manufacturing product parts in a single location for
global use at efficient scale, or mandating global consistency in brand positioning.
Concurrently, firms face a countervailing set of pressures to adapt their activities to the
unique circumstances of the countries in which they operate. These pressures for LR may
prompt responses such as manufacturing parts locally to obtain tax incentives or adapting
product positioning to local market circumstances.
Within the marketing function, companies attempt to deal with these conflicting
demand and cost pressures in a host of ways (Sheth, 2011). Some firms appear to be able to
find common segments across multiple markets and develop truly global brands with
underlying production efficiency. Pringles (Pollack, 1999) and Heinz (Neff, 1999) are two
brands able to standardize without major internal tradeoffs because customer needs vary little
across the globe. Other companies give in to the pressure of sacrificing global economies of
scale for the high levels of local adaptation they believe necessary to meet widely differing
local needs or circumstances. For example, many MNCs in the insurance business extensively
adapt their marketing to fit both market and regulatory requirements across different
countries. Other corporations seek the middle ground, standardizing some elements of the 4Ps
but allowing others to be adapted to the local market. For example, Philips attempted to
appeal to a global audience through its Olympic advertising (Edy, 1999) by delivering a
common message tailored to each market by different actors taking different approaches to
the use of different Philips products. Overall, the degree of local adaptation of the 4Ps
remains an important issue in MNC subsidiary strategy, both for scholars and managers. And
the choice of where an MNC subsidiary falls on this continuum is contingent on many
environmental factors and pressures, both at the global and local level. These include
customers, competitors, regulation, marketing infrastructure (distribution channels, support
9
agencies, etc.), technology, labour and the MNC‘s organisational culture and
internationalisation history (Cavusgil and Zou, 1994). The considerable diversity of these GI
and LR pressures—across nations, industries and firms—may also generate significant
heterogeneity in marketing at the subsidiary level. However, beyond discussions of the
stereotypical fully standardized or fully adapted firm, we have relatively little knowledge of
the shape and form of this heterogeneity. What are the empirical patterns of local adaptation
across MNC subsidiaries? Do most subsidiaries follow pure strategies like Pringles or hybrid
strategies like Philips? Is product standardization more prevalent than pricing standardization?
There is little evidence to answer these and many other questions, especially at the level of the
4Ps.
Local Innovation
Innovation is regarded as the fundamental basis for creating firm specific advantages that
enable a firm to achieve sustainable competitive advantage and improve its corporate
performance (Howard, 1993). With increasing global and local competition, there is a
growing imperative to tap into diverse sources of new ideas within the MNC network (Lee,
Chen, Kim & Johnson, 2008). In the context of MNCs therefore, the focus is increasingly
shifting to the MNC subsidiaries as the source of innovations (Bartlett & Ghoshal, 1986;
Gupta & Govindarajan, 1994). Although some subsidiary innovations may be created
specifically for the local subsidiary market, increasingly, subsidiaries are sources of
innovations that the MNC can leverage on a global basis. Local innovations span the entire
value chain, including customer facing product and marketing activities such as product
positioning, promotion, and sales and distribution. In this way, subsidiaries contribute to the
firm-specific advantages of the MNC, and thereby shift the generation of these advantages
―from being the sole concern of the parent company to a collective responsibility for the
corporate network‖ (Birkinshaw, Hood & Jonsson, 1998). Local innovation in MNC
10
subsidiaries is therefore seen as a critical and increasingly significant determinant of MNC
competitiveness. Moreover, marketing innovation can be seen, at least in part, as deriving
from interactions between the subsidiary and its local customers, distributors and support
agencies. Whether the result is price, product, promotion or place innovation, or a
combination of these, the innovation itself stems from the opportunities and challenges the
subsidiary sees in its local environment. As for local adaptation, and corresponding to the
diversity of these local environments, we might also expect significant heterogeneity in
innovation strategies across MNC subsidiaries. Perhaps some subsidiaries see opportunities
for promotional innovation, others for product innovation? And clearly there may also be a
relationship between local adaptation and local innovation. For example, an MNC with a
globally standardized product might not primarily seek product innovation from its
subsidiaries, but rather innovation in the areas of price, promotion or place. But again as for
local adaptation, we have little evidence to answer these questions, both in terms of the
patterns of local innovation across the 4Ps and any relationship between adaptation and
innovation.
Next, we discuss the two key ways in which MNCs take decisions, namely, local
autonomy and internal networking.
MNC SUBSIDIARY DECISION-MAKING
The locus of decision-making is an important issue since the way global strategies are
implemented within the network of MNC subsidiaries impacts on the performance of
multinational firms (Kashani, 1989). The major dimensions of organization structure are
complexity, centralization and formalization (Van de Ven, 1976). However, early studies
concentrated on the issue of centralization versus autonomy since centralization was regarded
as the primary construct in organization design (Egelhoff, 1988). More recently, there has
been a realization that centralization, while remaining important, may not fully capture the
11
wide range of methods and processes used by firms for taking decisions. For example, taking
decisions in multi-country teams and task forces is an important aspect of an MNC‘s
organizational structure and processes (Ghoshal, Korine & Szulanski, 1994). A network
approach to decision making is considered essential to gain deeper insights about the
complexities of the diverse markets served by large multinational firms and to respond rapidly
to changes in these markets. Following Ghoshal et al. (1994), we focus on two decision-
making constructs – local autonomy and internal networking. We view local autonomy as
reflecting the degree of decision-making freedom given to the subsidiary by the headquarters,
and internal networking as the degree to which the subsidiary uses, or is used by, other parts
of the firm for making key decisions. We now briefly review each of these two areas.
Local Autonomy
Notwithstanding extensive research in the MNC and organization literature on the issue of
locus of decision-making and its determinants and consequences, there is little research on
this issue in the international marketing literature until recently (e.g., Ozsomer & Simonin,
2004; Tong, Wong & Kwok, 2012). In the MNC literature, greater autonomy is considered to
have a strong motivating influence on the local subsidiary managers and encourages them to
take initiatives that result in marketing innovations for local and global markets. For example,
Bartlett and Ghoshal (1989) found subsidiary autonomy to have a positive relationship with
innovation in multinational firms. Birkinshaw et al. (1998) show that autonomy is associated
with the subsidiary contributing more towards firm-specific advantages at the global level, a
perspective also supported more broadly in the strategy literature (e.g., McGrath, 2001;
Zanfei, 2000). Venaik, Midgley and Devinney (2005) found local autonomy in marketing
decisions to have a significant positive effect on local marketing innovation. It might also be
argued that as subsidiaries are given greater autonomy a greater range of possibilities for
adaptation and innovation open up to them. The stereotypical centralized MNC implements
12
just one strategy across the globe. The stereotypical decentralized MNC may implement
many more strategies—up to and including a unique strategy for each of the local markets in
which it operates. Thus greater autonomy could contribute, at least potentially, to the
heterogeneity of strategies at the local level we discussed earlier. However, as before we have
little knowledge of what the actual patterns of centralization/autonomy are, especially at the
level of the 4Ps. Nor do we yet fully understand how autonomy relates to adaptation and
innovation at this level of detail.
Internal Networking
In the strategic management literature, organizational networks are classified into two broad
types – external and internal. External networks are formed between firms, whereas internal
networks are formed between organizational units separated by functions, businesses or
geographic locations (Charan, 1993). Here, we are interested in internal global networks as
mechanisms for organizational decision making in MNCs. That is, the extent to which
marketing decisions in the MNC are taken in groups, such as teams, task forces and
committees, comprising managers from the corporate and regional headquarters and country
subsidiaries.
Due to rapid technological change, the knowledge base of most businesses is becoming
increasingly complex and widely dispersed. Global networking increases the intensity of
communication among organisational members, which is ‗a major determinant of
organisation‘s effectiveness in creating and diffusing innovations‘ (Gupta & Govindarajan,
1991). Working across diverse customer, competitive, and country environments, subsidiary
managers bring together multiplicity of experiences and perspectives that assist in
overcoming narrow parochial functional, departmental or geographical interests, and
evaluating problems and taking decisions in the best interest of the global corporate
organisation (Charan, 1993). By providing a range of opinions from a variety of perspectives,
13
networking refines and ultimately increases the chances of success of the network-based
decisions (Powell, Koput & Smith-Doerr, 1996). Due to their flexibility, organisational
arrangements such as networks provide an effective means of quick decision-making in a
volatile environment, and are important sources of sustainable competitive advantage
(Charan, 1993; Powell et al., 1996). Cross-functional networks, such as teams and taskforces,
allow concurrent rather than sequential interaction, thus reducing the time-to-market for new
products and processes (Teece, 1996). Ghoshal et al. (1994) found significant positive
relationship between the use of networking mechanisms and inter-unit communication and
learning in MNCs. Overall, it appears MNCs increasingly use global networks for decision-
making, and to good effect, despite the increased complexity and coordination difficulties that
come with them. However, as yet we have relatively little understanding of the relative
frequency with which the 4Ps are discussed in these meetings. Nor whether there are distinct
and different patterns of internal networking across MNCs and their subsidiaries.
THE 7Ps FRAMEWORK, ARCHETYPAL ANALYSIS AND RESEARCH
HYPOTHESES
The preceding discussion demonstrates that these four areas—local adaptation, local
innovation, local autonomy and internal networking—are important topics for both scholars
and managers. Yet this discussion also raises a number of questions to which more detailed
answers are required. To begin to answer these questions, we first seek to develop a set of
reliable measures of the subordinate components that underlie local adaptation, local
innovation, local autonomy and internal networking. Such a set of measures allows us to look
at these topics at a finer level of detail. Second, using these measures and a sample of MNC
subsidiaries, we seek to identify the patterns that exist within each of the four areas. For
example, subsidiaries where products and promotions are standardized but prices and place
adapted. Or MNCs where decisions about products are discussed in networking meetings, but
14
never decisions about pricing or promotion. We identify such patterns using archetypal
analysis.
The 7Ps Framework
The set of measures for subordinate components we develop around a 7Ps framework. This
framework builds on marketing‘s 4Ps of price, product, promotion and place but adds three
additional components—product positioning, MNC marketing policies and marketing
personnel decisions. Product positioning is at the heart of the marketing function around
which the other 4Ps are built to deliver customer value and competitive advantage (Kotler,
1999). Despite its importance, positioning is often ignored in the international marketing
literature, as are marketing policies and personnel decisions – the process aspects of
marketing that are required to implement the 4Ps of the marketing program (Sorenson and
Weichmann, 1975). We use five – including the 4Ps and product positioning – to study local
adaptation and innovation—that is, activities visible in the local market. And we use all
seven – including policy and personnel – to study local autonomy and internal networking—
that is, MNC organization.
By appropriately framing our survey questions, this framework can be applied across all
the four areas of adaptation, innovation, autonomy and internal networking. We do this by
framing the questions in terms of the level of adaptation and innovation seen in, for example,
local promotion activities. And by asking further questions about the level of autonomy or
internal networking when local promotional decisions are made. As discussed in the
Methodology section each of these 5 or 7Ps is measured with multiple items and for each of
the four areas, resulting in a total of 24 reliable component measures. Thus our framework
aligns the major decisions in marketing with the key areas of interest to scholars and
managers and allows us to systematically compare these within and across subsidiaries.
Indeed, the values we measure on the 7Ps provide an interesting and more detailed profile of
15
each subsidiary in our sample. This also allows us to apply analytical techniques like AA to
identify patterns in these data.
However, before we discuss our hypotheses as to what we might expect to see in these
patterns, it is first necessary to outline archetypal analysis. This is because we need to justify
our choice of this technique but also because we need to show it permits the testing of more
interesting hypotheses about heterogeneity than other methods.
Archetypal Analysis
Explaining heterogeneity is an important aspect of scholarly work on international business.
Scholars do not assume their units of analysis are identical, be these countries, multinationals
or their subsidiaries. Rather scholars assume these vary in a systematic way, variation we seek
to explain by building appropriate theories. However, before we can explain heterogeneity we
must first describe it well. Such description requires the researcher (1) selects appropriate
variables with which to characterize their units of analysis and (2) conducts analyses that
illustrate and categorize the heterogeneity of these units in an insightful way. We believe we
meet the first requirement through the 7P component measures and so here we focus on the
second step. Why does AA provide better insights into these patterns of heterogeneity?
The word archetype itself means ―a very typical example of a certain person or thing‖
(Oxford Dictionary Online). This word derives from the Ancient Greek arkhe—meaning
primitive—and tupos—meaning a model. Archetypes are common in everyday language,
psychoanalysis, literature and art. For example, and in our context, the ‗centralized MNC‘ or
the ‗autonomous subsidiary.‘ Cutler and Breiman (1994) introduced archetypal analysis
(AA) as a formal statistical technique, motivating their analysis problem by a data set
containing the head measurements of a sample of Swiss soldiers. Was it possible to identify
the archetypal head shapes from these data—subject to the constraint that any individual
soldier‘s head could be represented as a mixture of these archetypes? They showed that this
16
was indeed possible. The reader is referred to the Technical Appendix for an explanation of
how AA works and the various issues the researcher must be aware of when applying it to
their data. The appendix also discusses why, in the context of our study, AA may be superior
to other pattern identification methods such as cluster analysis. Here we simply summarize
the key points from the appendix.
Although AA is not a cluster analysis method its output has some similarities, namely a
small number of discrete patterns—archetypes—that summarize the data. However, the big
difference of AA to other techniques is these archetypes have a clear definition. To
understand this definition, you need to imagine data as a cloud in the hyper-dimensional space
describe by the variables of interest (here the 7Ps). Archetypes are then defined as the
influential data points that best describe the exterior surface of this cloud. And largely
because of this clear definition, AA provides a number of advantages over commonly used
techniques such as hierarchical or k-means clustering, or even more recent developments such
as fuzzy clustering. These advantage include:
Sharper and more differentiated solutions than other techniques
AA imposes no strong ‗model‘ on the data
AA is robust to noise in the data
Each archetype is associated with a real observation, facilitating interpretation
All cases in the data have scores representing the degree they are associated
with each archetype—this allows ―single‖ and ―mixed‖ cases to be separated
(where ―single‖ cases are associated with only one of the archetypes and
―mixed‖ are associated with two or more archetypes).
Overall, AA produces simple, interpretable and robust solutions where the identified
archetypes and individual case scores have defined meaning. This contrasts with the
17
complexity, sensitivity to algorithmic assumptions and lack of interpretability of many other
techniques.
Research Hypotheses
Given our 5 and 7P measures and AA what might we expect to see in our data in terms of
numbers and profiles of archetypes across each of the four areas? Here we build on our
earlier discussion. Particularly the literature concerning the diverse pressures for global
integration and local responsiveness (Prahalad & Doz, 1987) and the heterogeneity of
subsidiary strategies and MNC organizations observed as a consequence (Bartlett & Ghoshal,
1989). From that literature, we expect to see the classic archetypes for fully standardized or
fully adapted subsidiaries, but we might also expect to see other archetypes that represent
composite strategies. That is, some of the 5Ps standardized and some adapted as in the
Philips example cited earlier. Unfortunately, beyond this simple conclusion, the literature is
not especially clear on how many archetypes we might expect in total, or what their profiles
across the 5Ps might look like. Similar comments can be made about the literature on local
innovation, autonomy and internal networking. So all we can say so far is we expect at least
three archetypes for each of the four areas. One archetype where all the relevant component
measures have high values, one where they have low values and one where they have a
composite of high and low values. However, we did also discuss the idea that there may be
more archetypes for strategy than for organization. This is on the basis that more autonomous
or less connected subsidiaries may develop a broader range of strategies—being freer to
choose which of the 5Ps they adapt or innovate. So here we might argue for a minimum of
four archetypes—the two classic and two composites. Based on this discussion, we propose
the following hypotheses on heterogeneity:
18
H1a: Three organization archetypes—classic high, classic low and composite
organizations--will be required to describe the observed heterogeneity in local
autonomy and internal networking.
H1b: Four strategy archetypes—classic high, classic low and two composite
strategies—will be required to describe the observed heterogeneity in local adaptation
and local innovation.
Next, from arguments around economies of scale, together with the examples cited before, we
might argue that one of the adaptation strategy composites would have a standardized product
and the other a standardized message. For example, where global scale is needed to develop
and manufacture a competitive product we might see a highly standardized product but local
adaptation on the other Ps. Where marketing requires scale, or where a globally consistent
message is desired, we might see a highly standardized positioning and promotion but local
adaptation on the other Ps. Similar arguments would apply to local innovation. From this
discussion we develop the following hypotheses:
H1c: For the case of local adaptation, one of the two composite strategies will have
standardized products and the other standardized positioning and promotion. For local
innovation, one will have no local innovation on product, the other no local innovation
on positioning and promotion. With the remaining Ps being more adapted or more
innovative as appropriate.
Hypothesis H1a can be rejected if we only find the two classic archetypes in our data or we
find more than three archetypes. In terms of AA we are postulating that three exterior points
will represent the surface of the 5 or 7-dimensional cloud to a reasonable approximation. For
Hypothesis H1b the relevant number is four archetypes/exterior points. Hypothesis 1c can be
rejected if we do not find composite archetype with the hypothesized profiles.
19
Thus far we have only examined heterogeneity as described by the archetypes
themselves. One advantage of AA is that, being a mixture technique; it also describes each
subsidiary as a mixture of archetypes. So we obtain a different perspective on heterogeneity
by looking at the association between subsidiaries and archetypes. Are most subsidiaries
‗pure,‘ that is, clearly associated with only one of the classic archetypes? Or are they
‗hybrid,‘ that is, associated either (a) with one of the composite archetypes or (b) with a
mixture of two or more archetypes? In terms of AA, this depends on the distribution of our
subsidiaries in the 5 or 7-dimensional space. Are the data points distributed close to the
exterior surface and therefore more likely to be clearly associated with an archetype? And if
so, is the archetype they are associated with classic or composite? Or are our data points
more towards the interior of the space and thus more likely to be mixture of two or more
archetypes? Given the scarcity of detailed data and the novelty of AA, the literature naturally
provides few answers to these questions. They might be seen more as empirical questions.
However, there is a general theme in the literature that firms often fail to make clear choices
of strategy, and are either stuck in the middle (Porter, 1980, 1985) or pursue hybrid strategies
and organization (Miller, 1992). Put together with the complexity and diversity of the global
business environment (Prahalad & Doz, 1987), we might well expect to see more subsidiaries
with hybrid strategies and organizations than those with pure ones. We therefore advance our
second, and admittedly more speculative, hypothesis on heterogeneity.
H2: The majority of MNC subsidiaries pursue hybrid strategies and organizations.
This hypothesis can be rejected if we see more pure than hybrid (mixed and composite)
cases in the data. In terms of AA, ‗pure‘ can be defined as an association score with any of
the classic strategy archetypes that is equal to or greater than 0.5. This is because the sum of
the association scores for one subsidiary across all archetypes totals to 1. A score of 0.5 or
greater therefore implies the subsidiary is more strongly associated with one archetype than
20
with any other archetype or with all the other archetypes together. Figure 1 summarizes this
discussion in a two by two matrix. In the Figure the label ‗pure‘ represents those subsidiaries
that are associated with one of the classic archetypes at a score of 0.5 or greater. The label
‗hybrid‘ represents either (1) those subsidiaries associated with a composite archetype at a
score of 0.5 or greater, or (2) those associated with a mixture of two or more archetypes—that
is, having scores of less than 0.5 with every archetype. The relative sizes of the various parts
of the Figure also depict the hypothesis—we expect to find more hybrid than pure cases.
==========================
FIGURE 1 ABOUT HERE
==========================
We now present the methodology we use to test these hypotheses.
METHODOLOGY
This section is organized as follows. First, we discuss the measures we use as inputs to AA
and the various steps we took to establish their reliability and validity. Second, we discuss our
unit of analysis, our sampling procedures, and the tests we use to identity any biases or
problems with our data. Third, we outline how we apply AA to our data.
Constructs and Measures
Here we seek to identify archetypes in four major areas of MNC subsidiary operations, so we
define our four study constructs as follows. (1) Local adaptation—the degree to which MNC
subsidiaries adapt their products, services and marketing activities to the demands of the local
market place. (2) Local innovation—the degree to which MNC subsidiaries innovate in their
products, services or marketing activities at the local level. (3) Local autonomy—the degree
to which MNC subsidiaries are given the freedom to make product, service and marketing
decisions at the local level. (4) Internal networking—the degree to which MNC subsidiaries
discuss product, service and marketing decisions with other subsidiaries or the head office of
21
their company. These four constructs are rich and complex, embodying multiple facets of
multinational organizations. To adequately capture their richness, the constructs are measured
with multiple questionnaire items using 7-point Likert scales. Exhibit 1 contains an extract of
the relevant sections of the questionnaire.
==========================
EXHIBIT 1 ABOUT HERE
==========================
We profile the four construct areas via a two-stage, component and item approach
(Chin, Marcolin & Newsted, 1996). The constructs of local adaptation and local innovation
are each measured with five marketing mix components of price, product, positioning, place
and promotion (5Ps). The constructs of local autonomy and internal networking are measured
with these five components plus two additional components for marketing policy and
marketing people (7Ps). In turn we measure each component with three to four questionnaire
items. For example, the price component of the local autonomy construct is measured by
asking the extent to which decisions pertaining to customer credit, price discounting, retail
pricing and wholesale pricing are made at the subsidiary or headquarters level. It is the
components themselves that become inputs to archetypal analyses; allowing us to identify
archetypes for each construct across the 5 or 7Ps. Analyses presented in the Technical
Appendix demonstrate these components are unidimensional and have adequate convergent
and discriminant validity for our purposes here.
Unit of Analysis, Sampling, and Tests of Potential Biases
Unit of analysis. MNC subsidiaries often operate in more than one area of business. To
focus our study, obtain more precise data, and reduce the time cost to the responding
manager, we chose a business unit within the subsidiary as our unit of analysis. We define a
business unit as an organizational unit that has separate and independent marketing and
22
profitability objectives. Within business units, we asked respondents to answer about the
product market with the highest annual sales revenue, assuming this to be most representative
of the business unit‘s activities. The key informant here is the head of the business unit.
Sample. A stratified random sample of MNE subsidiaries was selected from the Dun
and Bradstreet WorldBase. To ensure sufficient variance, strata included manufacturing and
services, consumer and industrial products, and subsidiaries in industrialized and
industrializing countries. Questionnaires were mailed to 1128 subsidiaries with a separate
questionnaire for each of the business units in the firm. The net response rate was 20 percent,
which compares favorably with the response rates of between 6 and 16 percent reported in the
literature for international surveys (Harzing, 1997). The responses we use here represent 229
business units; with an approximate 50:50 split between those operating in consumer and
those operating in business-to-business markets. Although the subsidiaries were located in 36
countries, their parent companies were mainly large Japanese, UK, and US MNCs with a
median of 22,000 employees worldwide and 325 employees in the subsidiary. Respondents
had an average of 10 years‘ experience in their company and averaged 40 years of age.
Potential bias. Although surveys are the standard approach to research in the
international business literature, questionnaire surveys inevitably raise concerns about
potential bias. Before analyzing our data we examined three such biases, namely measure
equivalence, common method bias, and non-response bias.
Measure equivalence. One potential bias in international studies concerns the degree to
which respondents from different countries interpret measures in the same way. For example,
this is a major issue in studies of individual values (Hui & Triandis, 1985). However, our
respondents were senior managers, mostly university educated, spoke English, traveled
widely, had been exposed to the business concepts incorporated in our measures, and were
familiar with questionnaire studies. While this suggests the potential for bias is low, we did
23
check the equivalence of our measures. First, for each subsidiary we computed Kogut and
Singh‘s (1988) cultural distance measure (here using the UK as reference point, the
questionnaire being in English because of its common use in MNCs and the impracticability
of translating the questionnaire into 30+ languages). Second, we ranked our subsidiaries by
cultural distance (low, medium, and high distance) and compared the means for our measures
between the high and low groups. After correcting for the known bias in multiple
comparisons, there are no significant differences between these means. Scale equivalence
problems in these data are unlikely to have biased our analyses to any significant extent.
Common method bias. Using a common 7-point scale across all measures can create a
response bias. Here this might also be exacerbated as constructs have a similar format because
of the use of common underlying components. However, factor analyses demonstrate that
there is no common factor loading on all measures (the ex post one-factor test, Podsakoff &
Organ, 1986). Further, the questionnaire itself contained intervening sections on other topics
and different phrasing of the questions for each construct. These are also steps that can reduce
common method bias (Podsakoff, Mackenzie, Lee & Podsakoff, 2003). Hence, although we
cannot rule it out, common method bias is unlikely in these data.
Non-response bias. To test for non-response bias, the original sample drawn from Dun
and Bradstreet and those subsidiaries that responded were compared on three criteria: the
number of countries, how long the subsidiary had operated, and the number of employees. We
received responses from subsidiaries in 60% (36 of 60) of the countries we sampled, so any
bias due to the countries included or excluded is likely to be small. Our data also covered all
continents. The median age (i.e., length of operation) and size of the subsidiaries responding
was 30 years and 325 employees versus 21 years and 250 employees for the non-respondents.
Overall, though these statistics suggest a slight bias to older and larger subsidiaries, we
believe our data set is more than adequate for our analyses.
24
Applying AA to Our Data
Our procedure for applying AA to our data is as follows.
Preliminary setup. First, we select the robust AA algorithm available within the R
package ―archetypal analysis‖ and follow the procedures described by the authors of this
package, Eugster and Leisch (2009), as well as the various steps outlined in the Technical
Appendix. Second, of the 229 business units responding to our questionnaire, we exclude 26
with missing data, leaving 203 complete questionnaires for further analysis. Third, we
identify multivariate outliers using standard techniques. Fourth, we ran preliminary archetypal
analyses—to identify whether any of these outliers had a strong influence on the selection of
archetypes. Five cases had a strong, distorting influence and are dropped—leading to a final
database of 198 MNC subsidiaries. Fifth, we generate 100 databases of normally distributed
random numbers; each with the same number of cases and variables, and the same scale
range, as the actual data. We use these databases to test whether the results of applying AA to
the actual data could occur by chance alone.
Archetypal analysis. For each set of variables, we examine solutions from one to ten
archetypes, repeating each analysis from 100 random starting points to reduce problems of
local minima. Our variables are standardized so they have equal weight in these analyses and
we take the best fitting solution from the 100 starting points. We also examine the warnings
from the package, none of which are of concern for the solutions we report here. Further, the
degree of fit (residual sum of squares) we obtain from different starting points is both
satisfactory and similar, suggesting there is no better fit to be found in these data.
Following practice in the AA literature, we use a scree plot of fit to determine the
number of archetypes to report. We also use the fit statistics from the 100 random databases
to identify a 1% confidence level. As an example, Figure 2 shows the fit to actual and random
data for the adaptation analysis (based on the 5P components of price, product, positioning,
25
place and promotion). Here the scree plot indicates six archetypes as the best solution. Also,
for the number of archetypes from two to eight, the fit we obtain from actual data is better
than the 1% confidence level by a substantial margin. In contrast, we cannot distinguish the
solutions for one, nine and ten archetypes from chance. Solutions for innovation, autonomy
and networking have similar scree plots, and for two to eight archetypes are also better than
chance. For brevity these results are not shown here but are available from the authors on
request. These scree plots indicate six archetypes for innovation (based on the 5Ps) and five
for autonomy and networking (based on the 5Ps plus policy and people). For those familiar
with cluster analysis, and the problem of slicing the database into small unstable clusters, we
should also point out this is not the case for AA. Our five and six archetype solutions are the
exterior cases/polytope vertices that best describe the complete cloud of 198 data points.
Given we can rule out local minima and chance, these are robust solutions.
==========================
FIGURE 2 ABOUT HERE
==========================
RESULTS
The results of applying AA to our data reject Hypotheses 1a and 1b. For organization, both
local autonomy and internal networking require five archetypes and not three as hypothesized.
For strategy, both local adaptation and local innovation require six archetypes and not four.
Further, examining the scree plots and the statistics on which they are based demonstrates
solutions with three or four archetypes could not be considered adequate in either case. While
there are as yet no formal tests for this conclusion, the reductions in the residual sum of
squares between three and five archetypes for organization are considerable. As are the
reductions between four and six archetypes in the case of the strategy constructs.
26
Figures 3 through 6 show the resulting archetypes. These we present as simple bar
charts, one chart for each archetype, five or six charts to a figure. All charts use the same
scaling from -3 to +3 standard deviations from the mean. Within each figure we also order the
individual archetypes. This we do from highly negative values on all components (Archetype
A) at the top left of the figure to highly positive values on all components at the bottom right
(F for Figures 3 and 4 with six archetypes, and E for Figures 5 and 6 with five archetypes).
==========================
FIGURES 3 – 6 ABOUT HERE
==========================
Thus for Figure 3, the six archetypes for local adaptation, archetypes A and F present
the biggest contrast. Archetype A is the classic subsidiary that adapts very little locally except
positioning to an average amount. The other four Ps are highly standardized. In contrast, F is
the classic subsidiary that adapts everything to a high degree. The remaining four archetypes
show more focused patterns of adaptation. B is a subsidiary that adapts just price and
promotion to an average extent, the rest being highly standardized. C is a subsidiary that
adapts price, and to an average extent place and product, but has highly standardized
positioning and promotion. D is a subsidiary that adapts product, place and promotion but has
relatively standardized positioning and highly standardized pricing. E is a subsidiary that
adapts everything except product, which is relatively standardized. Setting aside the greater
number of archetypes than hypothesized, there is some support for Hypothesis 1c in Figure 3.
Archetype E represents the hypothesized composite strategy with a standardized product but
all the other Ps locally adapted. And Archetype C represents the composite strategy of
standardized positioning and promotion and relatively more locally adapted place, product
and pricing.
27
Turning to Figure 4, the six archetypes for local innovation, reveals a similar but not
identical set of archetypes. The first and last charts in the figure are the classic subsidiaries
that are either not innovative at all (A) or highly innovative (F). This pattern is similar to that
for adaptation. However, the other, more focused, archetypes are not. B is a subsidiary that is
averagely innovative on promotion and place, but much less innovative on the other Ps. C is a
subsidiary somewhat similar to B except that it is noticeably more innovative on price. D is a
subsidiary that is innovative on promotion and positioning but below average on the other Ps.
And E is a subsidiary that is innovative on place, positioning and price but not on the other Ps.
There is some support for Hypothesis 1c in Figure 4, although this is not as strong as for local
adaptation. Archetype E comes closest to the hypothesized profile of no local innovation on
product, but local innovation on the other Ps. However, the promotion component with no
local innovation does not fit this hypothesis. And while archetype C is a composite strategy
with low innovation on positioning, only price is the focus of local innovation. The other Ps
having average levels of local innovation.
Turning to Figure 5, the five archetypes for local autonomy, we have two additional Ps
to consider, people and policy. Again the first and last charts are as before. Archetype A is the
classic subsidiary that is highly centralized and allowed very little autonomy on any of the
seven marketing decision areas. In contrast, E is the classic subsidiary with a high degree of
freedom to act in all seven areas. Interestingly, B is a similar subsidiary to A except it is given
more freedom in the areas of product and price. And D is a similar subsidiary to E except it is
given less freedom in the area of product. The middle archetype, C, is given less than average
autonomy to act on product, positioning and promotion, but greater than average autonomy on
people, policy and place.
Turning to Figure 6, the five archetypes for internal networking, the first and last charts
are similar to those for autonomy. We have a subsidiary, A, that is relatively unconnected
28
from the rest of the organization in all the seven decision areas. And we have the opposite in
subsidiary E that is highly connected in all seven areas. However, the remaining three
archetypes do not resemble those for autonomy. B is a subsidiary that, like A, is also
relatively unconnected but somewhat more connected in the area of people. C is also
relatively unconnected except in the area of product where it does network with the rest of the
organization. And D is a subsidiary, like E, that is relatively well connected to the rest of the
organization except to a lesser extent in the areas of place and price.
Table 1 summarizes the distribution of subsidiaries by archetypes across our four
constructs. The numbers under each archetype (A to F) represent the subsidiaries which have
an association of 0.5 or more with the respective archetype. The columns ‗Total Classic‘ and
‗Total Composite‘ show the total numbers of these strongly associated subsidiaries according
to whether they belong to two types of archetype. That is, either a classic archetype with an
extreme profile or a composite archetype with a non-extreme or middle profile. The classic
subsidiaries are associated with the archetypes A and F for adaptation and innovation and A
and E for autonomy and networking. The composite cases are strongly associated with the
remaining, non-extreme archetypes. The ‗Mixtures‘ column gives the total numbers of the
remaining subsidiaries. That is, those that do not have an association of 0.5 or greater with
any archetype and thus are mixtures of two or more archetypes, And the ‗hybrid‘ column
totals are the sums of the mixed and composite totals, in other words, the subsidiaries that are
not associated with the classic archetypes. As shown in the Table, the majority of our sample
subsidiaries are hybrids across all aspects of strategy and organization. As most subsidiaries
in our sample have hybrid strategies, the results in Table 1 provide strong support to
Hypothesis 2.
29
================================
TABLE 1 ABOUT HERE
================================
Figure 7 illustrates this conclusion for the adaptation measures by way of a parallel plot
of the six association scores for each subsidiary with each archetype. The density of the
plotted scores in the Figure show the archetypes A and F--the classic strategies of
standardization and adaptation respectively—are less representative of our sample than the
composite archetypes B to E. Further, many of the plot lines for individual subsidiaries are
placed towards the bottom of the figure. This indicates a large number of subsidiaries that are
mixtures of two or more archetypes.
Overall, the local adaptation strategy in MNC subsidiaries is more closely represented
by the composite archetypes (B to E) than by the classic ones (A and F). This is also the case
for local innovation, local autonomy and internal networking (Figures not shown but available
from the authors).
==========================
FIGURE 7 ABOUT HERE
==========================
DISCUSSION
First, we believe our results show AA to be a useful additional tool in the study of
multinational subsidiaries. AA provides a small number of profiles that summarize complex
data in a meaningful way. We should also not forget that, unlike cluster analysis, each of
these archetypes corresponds to one or more real subsidiaries. Moreover, there is a rationale
based in multidimensional topology for why these five or six archetype solutions are the best
summary of our data cloud. Finally, AA allows for subsidiaries to be described as mixtures of
archetypes, rather than forcing them into one profile.
30
Second, these results present challenges to conventional thinking about MNC strategy
and organization, particularly as they indicate greater heterogeneity than the literature
suggests. In our terms, more archetypes are necessary to describe the data cloud than might
be hypothesized from this literature. Thus we reject Hypotheses 1a of three organizational
archetypes and Hypothesis 1b of four strategy archetypes in favor of five and six archetypes
respectively. And Hypothesis 1c on the profiles of some of these additional, or ‗composite,‘
archetypes receives only modest support. There are archetypes in our data that cannot be
predicted from the existing literature. Further, and in contrast to the simplicity of the
literature, the majority of MNC subsidiaries are ‗hybrids.‘ That is, either represented by
composite (non-classic) archetypes or a mixtures of archetypes (interior data points).
An alternative way to visualize these results is to imagine the data cloud of subsidiaries
as a spikey ball where each of the spikes represents an archetype. The existing literature
focuses on some, but not most of the spikes on this ball. Nor does this literature focus on the
many subsidiaries in the interior of the ball. This analogy is not exact as our data cloud is in
five or six dimensions, but it does demonstrate the scale of the research challenge in
formulating better theories of MNC strategy and organization.
The detailed results themselves allow two conclusions and an open question. First,
while the archetypes that are highly negative on all variables (top left), or highly positive on
all variables (bottom right), are somewhat similar across the four constructs, the other
archetypes are not. We do see subsidiaries with these stereotypical patterns—that is, highly
standardized versus highly adapted, non-innovative versus innovative, centralized versus
decentralized and isolated versus networked. But we also see many other, more focused,
patterns where there are negative values on some variables but central or positive values on
others. In fact, of 22 archetypes, 14 are like this. Second, these focused patterns are not the
same across the four constructs—suggesting a subtlety, complexity and heterogeneity of
31
MNC subsidiary marketing that is not fully recognized in the literature. Third, the solutions
for the market-based constructs—adaptation and innovation—have six archetypes and those
for the organizational constructs—autonomy and networking—have five. We have examined
other numbers of archetypes for all four constructs but always reach the conclusion that six
and five are the right numbers. Essentially there is less multi-dimensional complexity in the
organizational constructs (despite them being based on two more variables) than the market-
based ones. Now it seems evident that where subsidiaries are given autonomy they will
develop more alternative strategies than where they are controlled. So we might expect more
complexity in the market-based constructs. But an interesting question for future research is
why only one more archetype is necessary to represent this added complexity? Might not
more be expected? Answering that question will of course require developing hypotheses
about the relationships between the four sets of archetypes. We may also need to incorporate
other variables; for example, descriptions of the local country environment, as these may play
an important role in the choice of adaptation or innovation strategy. Lastly, we also need to
develop explanations for those subsidiaries with hybrid strategies or organizations. What
leads decision makers to such configurations? And what are the performance consequences
of pure versus hybrid strategies or organizations?
Our study has the normal limitations, notably the cross-sectional nature of our survey
and the use of self-report data from one key informant. However, we believe it is strong
enough to suggest the international business literature needs to pay more attention to the
heterogeneity of the various marketing mix strategies and decision-making structures MNCs
employ across the globe.
32
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Table 1: Distribution of Subsidiaries by Archetypes and Degree of Association
Associated ≥ 0.5 with
Archetypes Total
Classic
Total
Composite
Mixtures
(≤0.5) Hybrid (%) Constructs A B C D E F
Local
Adaptation 10 7 6 6 25 48 58 44 96 140 (71%)
Local
Innovation 23 12 14 5 8 49 72 39 87 126 (64%)
Local
Autonomy 24 2 8 48 62 - 86 58 54 112 (57%)
Internal
Networking 38 13 30 18 57 - 95 61 42 103 (52%)
38
Figure 1: Associating Subsidiaries with Strategy Archetypes
39
Figure 2: Scree Plot for Local Adaptation
40
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F
-3 0 3
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A
-3 0 3
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B
-3 0 3
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C
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Price
Product
Posi oning
Place
Promo on
D
-3 0 3
Price
Product
Posi oning
Place
Promo on
E
Figure 3: Local Adaptation
41
-3.00 0.00 3.00
Price
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A
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Posi oning
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C
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E
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F
Figure 4: Local Innovation
42
-3.00 0.00 3.00
Price
Product
Posi oning
Place
Promo on
Policy
People
A
-3.00 0.00 3.00
Price
Product
Posi oning
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Policy
People
B
-3.00 0.00 3.00
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People
C
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Price
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Posi oning
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Policy
People
D
-3.00 0.00 3.00
Price
Product
Posi oning
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Policy
People
E
Figure 5: Local Autonomy
43
-3.00 0.00 3.00
Price
Product
Posi oning
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People
B
-3.00 0.00 3.00
Price
Product
Posi oning
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Policy
People
A
-3.00 0.00 3.00
Price
Product
Posi oning
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People
C
-3.00 0.00 3.00
Price
Product
Posi oning
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People
D
-3.00 0.00 3.00
Price
Product
Posi oning
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People
E
Figure 6: Internal Networking
44
Figure 7: Association Scores between MNC Subsidiaries and Adaptation Archetypes
45
Exhibit 1: Examples of Scales for Marketing Mix Strategy and Decision-Making in
MNC Subsidiaries
MARKETING MIX STRATEGY – LOCAL ADAPTATION
In subsidiaries of multinational firms, the marketing mix elements may be standardised (i.e.,
not modified for the local subsidiary market), or adapted (i.e., completely modified for the
local subsidiary market). Please indicate the extent to which the marketing mix elements for
your local subsidiary business unit are standardised or adapted. Circle the appropriate number
for each element on a scale of 1 to 7, where 1 means standardised, and 7 means adapted.
Product Brand Name--------------------------------------- 1 2 3 4 5 6 7
MARKETING MIX STRATEGY – LOCAL INNOVATION
Marketing innovation is defined as the extent to which a business unit seeks new ideas for
conducting its marketing activities and improving its marketing mix. Please indicate the extent
to which your local subsidiary business unit is innovative, i.e., seeking new ideas for
conducting the marketing mix activities. Circle the appropriate number for each activity on a
scale of 1 to 7, where 1 means not innovative, and 7 means highly innovative.
Product Brand Name-------------------------------------- 1 2 3 4 5 6 7
MARKETING MIX DECISION-MAKING – LOCAL AUTONOMY
In subsidiaries of multinational firms, the marketing mix decisions for the local subsidiary
business unit may be centralised (i.e., the decisions are never taken in the local subsidiary),
or autonomous (i.e., the decisions are always taken in the local subsidiary). Please indicate
the extent to which the marketing mix decisions for your local subsidiary business unit are
centralised or autonomous. Circle the appropriate number for each decision on a scale of 1 to
7, where 1 means centralised, and 7 means autonomous.
Product Brand Name Decisions-------------------------- 1 2 3 4 5 6 7
MARKETING MIX DECISION-MAKING – INTERNAL NETWORKING
Networks are defined as groups, such as teams, task forces, meetings, committees, etc.,
comprised of managers from the corporate and regional headquarters, and the various
country subsidiaries of the parent company. Please indicate the extent to which the marketing
mix decisions for your local subsidiary business unit are taken in networks. Circle the
appropriate number for each decision on a scale of 1 to 7, where 1 means never taken in
networks, 7 means always taken in networks.
Product Brand Name Decisions ------------------------- 1 2 3 4 5 6 7
46
TECHNICAL APPENDIX
This appendix is divided into two sections, A and B.
Section A outlines archetypal analysis (AA) and discusses issues in applying this
technique to the data typically obtained from surveys of international business. Section A
also presents arguments why we believe AA is an appropriate and strong technique for
describing heterogeneity in such data.
Section B details the tests of convergent and discriminant validity we applied to our
data, both at the level of individual construct measures and for the archetypes we develop
from these measures. Section B also presents some comparisons between standard cluster
analysis solutions and our archetypes. These comparisons also help us to demonstrate the
superiority of AA over these standard clustering approaches.
A: Archetypal Analysis
In our view, the best way to explain AA is to start from cluster analysis, the basics of which
many people know. AA is not a clustering approach, but discussing it in this manner
highlights the key conceptual difference between AA and cluster analysis, a difference that
underlies the potential superiority of AA for our purposes.
Cluster analysis. Here we will only outline two of the main techniques. In doing so, we
will briefly examine the assumptions underlying these techniques and the usefulness of the
clusters they generate. This will set the context for our discussion of AA. We should note we
have simplified this discussion to a few key points. Cluster analysis is a large and complex
topic. The reader is referred to a text such as Kaufman and Rousseeuw (1990) for details on a
broader range of techniques and more depth on the statistical foundations of clustering. The
two basic techniques we outline are (1) hierarchical clustering and (2) centroid clustering. As
for most clustering techniques, both use a measure of the ‗distances‘ between the units of
analysis to determine which units are ‗closest‘ and therefore most similar to each other. This
47
distance is computed across all the variables of interest, typically using either the Euclidean or
Manhattan (‗city-block‘) metrics. For example, using the Manhattan metric the distance
between two units is simply the sum of the absolute differences between the two values of
each of the variables describing these units.
Hierarchical clustering (Ward 1963) assigns the units of analysis into a tree structure
(‗dendrogram‘). This structure is generated by merging those units closest to each other into
‗twigs,‘ those twigs closest to each other into ‗branches‘ and so forth into the main ‗trunk‘ of
the tree. This tree can be built bottom-up (‗agglomerative‘ clustering) or top down (‗divisive‘
clustering.) But whether bottom-up or top down, to arrive at the final tree we require
additional assumptions on how we link clusters to each other. For example, do we assume the
distance between two clusters is that between the two ‗nearest neighbor‘ units or do we
assume it is the average of the distances between all the units in the first cluster and all those
in the second? Different assumptions often produce quite different tree structures.
One advantage of hierarchical clustering is each cluster is derived from a real
observation and is therefore readily interpretable. One disadvantage is the tree structure itself
is often unwieldy for practical purposes as it presents a complete picture of all the data.
Choosing a simpler structure from the tree can then become more of a subjective judgment on
the part of the researcher than an outcome of the analysis itself. For that reason, systematic
methods to help with this simplification have been developed (i.e. Boudaillier & Hebrail,
1998).
The second technique is centroid clustering, typically the k-means variant (MacQueen
1967). With k-means the researcher specifies the number (k) of clusters they want and
supplies starting or ‗seed‘ units for each of these (typically randomly selected). The algorithm
then identifies which other units are closest to each seed unit and designates these as the
starting clusters. The means of each cluster on the set of variables—the centroids—are then
48
computed and used as reference coordinates for a new set of distances. This process repeats
itself until the solution converges to a local minimum, with the objective of maximizing the
similarity of the units within each cluster.
One advantage of k-means is that it provides a simple clear description of heterogeneity
by way of a small number of clusters. This simple solution may be the reason k-means
appears to be more popular for business applications than hierarchical clustering. One
disadvantage is the centroid of a cluster may not resemble any real observation, blurring
interpretation. For this reason, more recent algorithm use mediods rather than centroids,
where the mediod is the observation with the smallest average distance to the other
observations in the cluster (Van der Lann, Pollard & Bryan, 2003).
From this discussion of hierarchical and centroid clustering, it can be seen we would
often prefer simple cluster solutions where each cluster can be accurately represented by a
real observation rather than a statistic. And we would prefer these solutions to be less
sensitive to the algorithmic assumptions we make. It is also notable that for both of these
techniques to work well requires (1) clusters that are clearly distinct from each other and (2)
units that clearly belong to one cluster. Unfortunately real data is often not like this. For
example, variables may not strongly discriminate between units, ―clusters‖ may overlap and
units may ―belong‖ to more than one cluster or be isolated from all clusters. The assumption
of ―lumpy clouds‖ of data in some highly dimensional space is a strong one that may not
always be valid (Elder & Pinnell, 2003). And when this assumption is not valid, both
hierarchical and centroid clustering will fail to produce useful solutions.
Moreover, unambiguously assigning each unit to a cluster is not the only way to
describe multivariate data. More recent techniques—such as fuzzy clustering—take a
different approach. Fuzzy clustering (Bezdek, 1981) does this by describing each unit as
having varying degrees of membership to all the clusters identified in the data. This
49
introduces the idea of probabilistic mixtures as a viable alternative to unambiguous
assignment. Instead of saying a unit is either of cluster X or Y, we say it is 70% X and 30%
Y. This mixture approach is potentially both more flexible and more realistic given the data
we typically have available in international business.
In addition, mixtures have more in common with the nuanced ways in which human
beings think. Indeed, reasoning from universally understood prototypes and seeing other
objects as mixtures of these is common in everyday activity and language (for example,
‗…there‘s a little bit of __________ in everyone‘.). The idea of such universal prototypes—
archetypes—also has a long history in philosophy (Plato) and psychology (Jung). Archetypal
analysis builds on these ideas and is based on a mixture model. We argue it is an
improvement on all forms of clustering, including fuzzy clustering, because it has a
conceptual definition of ‗archetype‘ that builds on the topology of the data. In contrast, fuzzy
clustering (and other more recent clustering methods) continue in the mainstream clustering
tradition of defining ―clusters‖ primarily through the features of the specific algorithm used.
We will return to this point shortly, first it is necessary to outline the main features of AA.
Archetypal analysis. Cutler and Breiman (1994) introduced AA as a formal statistical
technique. They motivated the analysis problem by reference to data on the head
measurements of 200 Swiss soldiers. Was it possible to identify ―pure‖ or archetypal head
types in these data, with the constraint that each individual soldier‘s head should be
represented as a mixture of these archetypes? To achieve this goal requires an algorithm that
approximates each real head by a mixture, while maximizing the overall fit to the data. But to
find these mixtures requires we first identify the ‗archetypes‘ that generate them. And to
identify the archetypes requires we have a definition of what they are. The main insight of
Cutler and Breiman was to formally define their archetypes through the topology of the data.
50
To understand this topological definition, we first need to imagine our data as a cloud of
points in the multidimensional space described by our set of variables. Next we enclose this
data cloud by a hypersurface whose vertices are exterior data points of the cloud. The
hypersurface defined by the minimum number of such data points is called the convex hull of
the data. In two dimensions we can use the analogy of snapping a rubber band around an
object—the taut band becomes the convex hull of that object. AA approximates this convex
hull by a simpler polytope (a polytope is the multidimensional extension of a polyhedron).
The vertices of this polytope are the archetypes. They are also data points and the polytope
itself encompasses a smaller hypervolume than the convex hull. Data points lying inside the
convex hull are exact mixtures of archetypes, while points lying outside the polytope are only
approximated (Cutler & Breiman, 1994). This process of approximating the convex hull by a
simpler polytope has analogies to many statistical techniques where we use a reduced form to
capture most but not all of the information in the data. For example, choosing a small number
of factors in principal components analysis or dropping non-significant coefficients from a
regression. Generally the number of archetypes is much smaller than the number of vertices in
the convex hull (Chan, Mitchell & Cram, 2003), allowing a more parsimonious
representation.
In summary then, archetypes are the specific exterior points that best account for the
shape of the data cloud. In our opinion, this topological definition of the pure types in the
mixture makes AA superior to other mixture models such as fuzzy clustering. The archetypes
in AA are defined more in terms of the overall shape of the data cloud than through the
operation of the algorithm. And largely because of this definition, AA provides a number of
advantages over the commonly used techniques such as hierarchical or k-means clustering.
First, AA can produce solutions that are sharper and more differentiated than k-means
or hierarchical clustering (Li et. al., 2003; Elder & Pinnel, 2003). Second, each archetype is
51
associated with a real observation, facilitating interpretation (Elder & Pinnel, 2003). Third,
the degree of association of an individual unit is defined against the standard of the archetype
rather than a less meaningful cluster centroid (Elder & Pinnel, 2003). Fourth, AA imposes no
restrictions of orthogonality (Cutler & Breiman, 1994), nor does it impose a strong ‗model‘
on the data (Li et al., 2003). Rather the key modeling assumption is that a simple polytope is a
good representation of the cloud. Finally, simulations have shown AA is robust in the
presence of Gaussian, Poisson and systematic error noise in the data (Chan et al., 2003).
Overall, AA produces simple, interpretable and robust solutions where the key vectors—the
archetypes—have defined meaning and key assumption is the polytope. This contrasts with
the complexity, sensitivity to algorithmic assumptions and lack of interpretability of many
clustering approaches.
That said, it is necessary to clear up some lack of clarity in the literature and note some
disadvantages of this technique. First, the exterior data points defining the archetypes are not
outliers or extreme cases (here we present a different point of view to Li et al., 2003). AA is
just as vulnerable to the influence of outliers as any statistical technique (Eugster & Leisch,
2010). Extreme data points—i.e. ones far from the main cloud of data—may distort the
archetypes by extending the polytope well beyond the cloud. The researcher should either
exclude these outliers from their analysis or use robust archetypal methods (Eugster & Leisch
2010). Second, like many other techniques using numerical optimization, the alternating least
squares algorithm that Cutler and Breiman utilize to fit the polytope to data provides no
guarantee of finding a global minimum. And for AA this problem appears to get worse as the
number of archetypes increases (Cutler & Breiman, 1994; Elder & Pinnell, 2003).
Researchers need to (1) use an adequate range of random starting points and (2) demonstrate
the minimum they obtain is not the result of statistical chance. While such steps are desirable
whenever numerical optimization is used, for AA this may be a necessity. Third, AA is a
52
relatively new technique with only a limited number of applications in the literature to date
and only a few published simulation studies investigating its properties. More research is
clearly needed before it becomes a standard technique.
B: Tests of Convergent and Discriminant Validity
The correlations in Tables A1 to A4 show our measures have high convergent and
discriminant validity. Individual items are considered to have convergent validity if they
correlate more than 0.7 with the component that they intend to measure. All items satisfy this
criterion. Similarly discriminant validity is evaluated by examining the cross-loadings of the
items and their components. Examining Tables A1 to A4, the correlations of the components
with their items are higher than those with the items associated with other components.
Simple factor analyses also demonstrate all components are one-dimensional, which is a basic
requirement for these component measures and statistics to be valid.
=============================
TABLES A1 – A4 ABOUT HERE
=============================
As noted before, we define a subsidiary to be associated with an archetype if its score
for that archetype is 0.5 or higher. This seems reasonable as AA normalizes the association
scores of a subsidiary with all the archetypes to sum to 1. Hence, an association of 0.5 or
more with a single archetype implies the subsidiary does not have a higher association with
any other archetype or all the other archetypes together. To examine the convergent and
discriminant validity of our AA results, we separate our sample into (1) those subsidiaries that
are clearly associated with a single archetype (score of 0.5 or more) and (2) those that are
mixtures (all scores less than 0.5). For the first sample, we test if the average association with
their respective archetypes is significantly higher than their average association with the other
archetypes. In a table of such averages we should see the same pattern as in the standard
53
tables of convergent and discriminant validity shown earlier. That is, higher values in the
diagonal cells and lower values in the off-diagonal cells. This is indeed the pattern we see in
Tables A5 to A8. Moreover, for 21 of the 22 archetypes we see in these figures the on-
diagonal values are also significantly higher than the off-diagonal values. We perform this
test by computing the 95% lower confidence limit for the diagonal values and checking
whether all off-diagonal values are below this number. We use the t-distribution here because
of the small numbers of subsidiaries allocated to some cells. Thus we have some basis for
concluding there is adequate convergent and discriminant validity for 21 of the 22 archetypes
we present here. The one exception is archetype B for autonomy—there is a possibility this
composite archetype overlaps with another
============================
TABLES A5 – A8 ABOUT HERE
=============================
For the second sub-sample of subsidiaries associated with a mixture of archetypes, the
average subsidiary weights are nearly uniformly distributed across all archetypes for each of
the four constructs. For example, for the 96 subsidiaries with mixed adaptation strategies, the
average association with each of the six archetypes ranges from 0.13 to 0.25. Similarly, the
87 subsidiaries with mixed innovation strategies have average association ranging from 0.08
to 0.27. Turning to organization, the average association for the 54 mixture subsidiaries in
autonomy and the 42 mixture subsidiaries in networking ranges from 0.13 to 0.25 and 0.10 to
0.27 respectively. Thus, overall, the average association for the subsidiaries with mixed
strategies or organizations does not exceed 0.3 on any archetype. Here we cannot say
anything about convergent or discriminant validity because there is no referent. What we can
say is the interior data in our cloud are quite distinct from the exterior.
54
Comparing Archetypal Analysis (AA) and Cluster Analysis (CA)
Although AA has a totally different conceptual basis to CA, it is often compared with CA (Li
et al., 2003) and the archetypes in AA considered to be similar to clusters in CA. This is not
really the case. To illustrate the differences between AA and CA, we cluster the cases based
on the component measures for each of the four constructs and compare the resulting
solutions with the archetypes reported earlier. Since these comparisons produce similar
conclusions across all four constructs, we only report those for the adaptation construct here
(the comparisons for the other constructs are available from the authors). To make the
comparisons we apply a standard clustering algorithm—k-means—to the adaptation
component measures and generate a six-cluster solution to match the six archetypes we
extracted earlier. We then compare the cluster profiles with the corresponding archetype
profiles. As shown in the last column of Table A9, the profiles of cluster 3 and archetype C
are negatively related (r=-.84) and those of cluster 4 and archetype D only weakly related (r=
.26). In contrast, the profiles of clusters 1 and 2 and the corresponding archetypes A and B
are more strongly correlated (r = .55 and r = .68 respectively) and the profiles of clusters 5
and 6 and the corresponding archetypes E and F are highly correlated (r = .95 and r = .98
respectively). From correlations between profiles, we might therefore conclude there is a
relationship between the two solutions but they are by no means identical.
=========================
TABLE A9 ABOUT HERE
==========================
However, as well as correlating profiles we also need to see how the two techniques
classify the various subsidiaries. To examine this point we cross-tabulated the cases
classified into each of the six clusters and archetypes. This cross-tabulation is shown in Table
A10 and as previously separates those subsidiaries clearly associated with an archetype from
55
those that are mixtures of two or more archetypes. This separation clarifies the difference
between standard CA and AA and shows what drives the correlations in Table A9. These
correlations originate from those subsidiaries clearly associated with an archetype—74 of 102
matching in the same pattern as the correlations (namely, clusters 1, 2, 5 and 6 with
archetypes A, B, D and E). However, what is missing in CA and provided by AA is the
identification of 96 subsidiaries with mixed strategies. AA thus adds a level of insight into
the data that is missing in CA. Moreover, the CA solution can be seen to be misleading as
subsidiaries with mixed strategies are assigned to one cluster. Fuzzy clustering techniques
would ameliorate this problem but lack the clear definition the exterior points of AA provide.
It is for all these reasons we recommend AA as a technique well worth considering in
international business research.
=========================
TABLE A10 ABOUT HERE
==========================
TECHNICAL REFERENCES
Bezdek, J. 1981. Pattern Recognition with Fuzzy Objective Function. Plenum Press, New
York.
Boudaillier, E. & Hebrail, G. 1998. Interactive Interpretation of Hierarchical Clustering,
Intelligent Data Analysis, 2, 229-244.
Cutler, A. & Breiman, L. 1994. Archetypal Analysis. Technometrics, 36(4), 338-347.
Elder, A & Pinnel, J. 2003. Archetypal Analysis: An Alternative Approach to Finding and
Defining Segments. Proceedings of the Sawtooth Software Conference, Sequim, WA,
June, 113-132.
Eugster, M.J.A. & Leisch, F. 2010. Weighted and Robust Archetypal Analysis. Technical
Report Number 082. Department of Statistics University of Munich.
56
Kaufman, L & Rousseeuw, P. 1990. Finding Groups in Data: An Introduction to Cluster
Analysis. New York: John Wiley & Sons.
Li, S. Wang, P. Louviere, J. & Carson R. 2003. Archetypal Analysis: A New Way to Segment
Markets Based on Extreme Individuals. ANZMAC Conference Proceedings, Adelaide,
December, 1674-1679.
MacQueen, J. B. 1967. "Some Methods for classification and Analysis of Multivariate
Observations". Proceedings of 5th Berkeley Symposium on Mathematical Statistics
and Probability. University of California Press. 281–297.
Van Der Lann, M. J. Pollard K. S. & Bryan, J. E. 2003. A New Partitioning Around Medoids
Algorithm. Journal of Statistical Computation and Simulation. 73 (8), 575–584
Ward, J. H. 1963. "Hierarchical Grouping to Optimize an Objective Function". Journal of the
American Statistical Association, 58 (301), 236–244.
57
Table A1: Convergent and Discriminant Validity – Local Adaptation
Local Adaptation Component
Local Adaptation Item Product Price Place Promotion Positioning
Product brand name .77 .17 .21 .25 .21
Product design .84 .24 .28 .24 .36
Product range .81 .35 .36 .38 .38
Product packaging .82 .26 .28 .25 .21
Retail price .32 .84 .43 .19 .23
Wholesale price .34 .85 .43 .10 .19
Customer credit .17 .80 .43 .21 .22
Price discounting .25 .86 .48 .23 .25
Sales force decisions .30 .39 .82 .40 .32
Channel decisions .34 .44 .84 .35 .40
Inventory management decisions .34 .40 .79 .21 .29
Physical distribution decisions .21 .47 .85 .37 .36
Advertising theme .39 .11 .30 .84 .45
Advertising copy .33 .14 .31 .89 .34
Media mix .28 .25 .41 .84 .36
Sales promotion .23 .23 .40 .84 .41
Market segmentation .31 .28 .41 .38 .91
Target segments .32 .21 .40 .41 .94
Product positioning .36 .23 .32 .44 .91
58
Table A2: Convergent and Discriminant Validity – Local Innovation
Local Innovation Component
Local Innovation Item Product Price Place Promotion Positioning
Product brand name .72 .18 .23 .22 .22
Product design .88 .12 .10 .28 .24
Product range .81 .16 .14 .29 .29
Product packaging .79 .29 .29 .31 .31
Retail price .28 .89 .43 .21 .41
Wholesale price .17 .91 .47 .15 .47
Customer credit .16 .77 .60 .34 .36
Price discounting .20 .86 .52 .31 .38
Sales force decisions .23 .46 .79 .54 .51
Channel decisions .21 .54 .86 .45 .44
Inventory management decisions .16 .48 .84 .40 .35
Physical distribution decisions .18 .50 .86 .39 .40
Advertising theme .38 .12 .37 .88 .37
Advertising copy .38 .17 .40 .92 .34
Media mix .23 .38 .54 .87 .44
Sales promotion .24 .36 .54 .79 .48
Market segmentation .24 .41 .43 .40 .93
Target segments .27 .42 .45 .40 .95
Product positioning .40 .40 .44 .44 .85
59
Table A3: Convergent and Discriminant Validity – Local Autonomy
Local Autonomy Component
Local Autonomy Decision Item
Pro
du
ct
Pri
ce
Pla
ce
Pro
moti
on
Posi
tion
ing
Poli
cy
Peo
ple
Product brand name .81 .29 .27 .32 .32 .29 .17
Product design .89 .34 .31 .37 .37 .31 .16
Product range .82 .42 .43 .43 .48 .39 .27
Product packaging .83 .41 .29 .39 .37 .33 .19
Retail price .42 .82 .52 .39 .36 .42 .41
Wholesale price .42 .87 .60 .32 .45 .47 .44
Customer credit decisions .27 .76 .59 .38 .36 .54 .56
Price discounting .37 .86 .59 .38 .37 .45 .47
Sales force .27 .53 .80 .52 .42 .60 .63
Channel .36 .57 .83 .59 .61 .65 .61
Inventory .34 .53 .81 .37 .39 .51 .53
Physical distribution .35 .58 .86 .43 .40 .54 .56
Advertising theme .47 .27 .39 .84 .55 .56 .45
Advertising copy .48 .31 .46 .92 .54 .61 .50
Media mix .40 .49 .61 .90 .60 .69 .59
Sales promotion .27 .48 .64 .82 .57 .64 .61
Market segmentation .41 .45 .54 .61 .94 .70 .50
Target segments .39 .41 .51 .54 .93 .65 .50
Product positioning .48 .39 .46 .62 .90 .65 .48
Marketing policy .36 .47 .60 .63 .65 .87 .63
Market research .35 .45 .55 .65 .68 .88 .58
Marketing budget .31 .48 .63 .55 .55 .88 .71
Marketing personnel selection .22 .55 .66 .53 .47 .71 .94
Marketing personnel training .23 .54 .64 .59 .50 .70 .94
Marketing personnel performance evaluation .19 .43 .60 .49 .46 .63 .90
60
Table A4: Convergent and Discriminant Validity – Internal Networking
Internal Networking Component
Internal Networking Decision Item
Pro
du
ct
Pri
ce
Pla
ce
Pro
moti
on
Posi
tion
ing
Poli
cy
Peo
ple
Product brand name .80 .18 .22 .35 .42 .37 .25
Product design .90 .28 .27 .35 .51 .42 .26
Product range .89 .50 .48 .55 .58 .56 .44
Product packaging .83 .48 .49 .63 .56 .54 .51
Retail price .41 .90 .73 .65 .55 .62 .62
Wholesale price .43 .94 .81 .68 .62 .65 .69
Customer credit .36 .91 .80 .70 .57 .64 .72
Price discounting .34 .93 .82 .70 .62 .63 .66
Sales force .36 .83 .92 .69 .65 .66 .73
Channel .42 .84 .93 .75 .67 .70 .75
Inventory .35 .71 .89 .55 .61 .60 .64
Physical distribution .40 .77 .93 .64 .60 .60 .68
Advertising theme .56 .59 .59 .88 .70 .64 .56
Advertising copy .52 .66 .64 .95 .71 .70 .65
Media mix .49 .71 .69 .94 .67 .72 .71
Sales promotion .40 .74 .76 .87 .68 .66 .69
Market segmentation .54 .59 .66 .70 .96 .73 .67
Target segments .55 .62 .68 .72 .98 .74 .68
Product positioning .60 .59 .64 .71 .95 .73 .63
Marketing policy .48 .56 .57 .58 .68 .88 .63
Market research .53 .58 .58 .69 .69 .89 .71
Marketing budget .42 .65 .66 .67 .65 .90 .73
Marketing personnel selection .38 .68 .69 .68 .64 .76 .95
Marketing personnel training .39 .69 .72 .66 .64 .75 .94
Marketing personnel performance evaluation .37 .65 .68 .63 .63 .68 .93
61
Table A5: Convergent and Discriminant Validity Analysis of Case Archetypes (Local
Adaptation)
Adaptation
archetype N
Mean weight of cases in each
archetype Total
weight
SD for
max
weight
95% LCL
for max
weight
Is LCL above
mean weight
of all other
archetypes? A B C D E F
A 10 .71 .07 .08 .09 .03 .01 1.00 .21 0.56 Yes
B 7 .09 .72 .08 .02 .05 .04 1.00 .20 0.54 Yes
C 6 .08 .08 .69 .03 .09 .02 1.00 .17 0.52 Yes
D 6 .12 .05 .03 .65 .11 .03 1.00 .18 0.45 Yes
E 25 .09 .03 .07 .06 .64 .11 1.00 .12 0.59 Yes
F 48 .05 .05 .05 .04 .07 .75 1.00 .16 0.70 Yes
TOTAL 102
(Due to rounding, total weight varies slightly from the simple sum of weights)
(SD – standard deviation, LCL – lower confidence limit)
62
Table A6: Convergent and Discriminant Validity Analysis of Case Archetypes (Local
Innovation)
Adaptation
archetype N
Mean weight of cases in each
archetype Total
weight
SD for
max
weight
95% LCL
for max
weight
Is LCL above
mean weight
of all other
archetypes? A B C D E F
A 23 .65 .07 .05 .05 .06 .12 1.00 .16 0.58 Yes
B 12 .13 .65 .02 .05 .04 .11 1.00 .14 0.56 Yes
C 14 .13 .02 .63 .04 .02 .15 1.00 .14 0.55 Yes
D 5 .01 .04 .14 .70 .04 .08 1.00 .20 0.45 Yes
E 8 .04 .11 .06 .02 .67 .09 1.00 .20 0.51 Yes
F 49 .12 .06 .08 .03 .05 .66 1.00 .14 0.62 Yes
TOTAL 111
(Due to rounding, total weight varies slightly from the simple sum of weights)
(SD – standard deviation, LCL – lower confidence limit)
63
Table A7: Convergent and Discriminant Validity Analysis of Case Archetypes (Local
Autonomy)
Autonomy
archetype N
Mean weight of cases in
each archetype Total
weight
SD for
max
weight
95% LCL
for max
weight
Is LCL above
mean weight
of all other
archetypes? A B C D E
A 24 .69 .07 .09 .03 .12 1.00 .16 0.62 Yes
B 2 .00 .77 .01 .22 .00 1.00 .33 -2.19 No
C 8 .08 .03 .73 .05 .11 1.00 .12 0.62 Yes
D 48 .12 .05 .07 .67 .10 1.00 .12 0.63 Yes
E 62 .08 .05 .05 .10 .72 1.00 .15 0.68 Yes
TOTAL 144
(Due to rounding, total weight varies slightly from the simple sum of weights)
(SD – standard deviation, LCL – lower confidence limit)
64
Table A8: Convergent and Discriminant Validity Analysis of Case Archetypes
(Internal Networking)
Networking
archetype N
Mean weight of cases in
each archetype Total
weight
SD for
max
weight
95% LCL
for max
weight
Is LCL above
mean weight
of all other
archetypes? A B C D E
A 38 .74 .02 .09 .03 .12 1.00 .17 0.68 Yes
B 13 .10 .64 .06 .12 .08 1.00 .10 0.58 Yes
C 30 .15 .05 .62 .07 .11 1.00 .10 0.58 Yes
D 18 .05 .09 .12 .63 .11 1.00 .12 0.57 Yes
E 57 .07 .05 .08 .06 .74 1.00 .17 0.69 Yes
TOTAL 156
(Due to rounding, total weight varies slightly from the simple sum of weights)
(SD – standard deviation, LCL – lower confidence limit)
65
Table A9: Comparing Cluster and Archetype Profiles
Profiles on adaptation components Correlation
CA/AA
profiles Cluster Archetype Price Product Positioning Place Promotion
1
-1.58 -0.78 -1.34 -2.08 -1.72 0.55
A -2.48 -1.36 -0.41 -2.15 -2.63
2
0.16 -0.91 -1.30 -0.59 -0.72 0.68
B 0.30 -1.46 -2.28 -2.14 0.23
3
-1.73 -0.52 0.17 -0.22 0.31 -0.84
C 0.84 -0.38 -2.35 0.19 -2.81
4
0.07 0.75 -0.28 -0.15 -0.19 0.26
D -2.11 1.04 -1.00 0.58 1.09
5
0.50 -0.49 0.44 0.49 0.26 0.95
E 0.84 -1.50 1.14 0.97 1.09
6
0.62 1.39 0.97 0.74 0.83 0.98
F 0.84 1.88 1.14 0.84 1.09
Cluster and archetype profiles generated from k-means and robust archetypal analyses of 198 MNC subsidiaries.
Table A10: Cross Tabulation of CA versus AA
Adaptation archetypes
(Scores of 0.5 or more)
Mixtures
Total
cases Clusters A B C D E F
1 8 1 1 0 0 0 5 15
2 0 6 5 0 0 0 16 27
3 0 0 0 1 0 13 23 37
4 2 0 0 5 0 0 14 21
5 0 0 0 0 25 0 37 62
6 0 0 0 0 0 35 1 36
Total 10 7 6 6 25 48 96 198
