An Industry Explanation of
Global Account Management
Professor David Montgomery, Professor George Yip,
Dr Belen Villalonga
Centre for the Network Economy
CNE WP09/2002
This is an advance copy of working paper CNE WP 09/2002. It is provided as a service to
Gold level sponsors of the Centre for the Network Economy for 3 months prior to publication. This paper may be distributed within Andersen and Lucent Technologies
(including Intranet) but must not be passed to people outside these organizations before 13
May 2002, except with the written permission of the authors.

AN INDUSTRY EXPLANATION OF GLOBAL ACCOUNT MANAGEMENT
3 January 2002
David B. Montgomery, 1 George S. Yip, 2 and Belen Villalonga 3
1
S. S. Kresge Professor of Marketing Strategy, Emeritus
Stanford Graduate School of Business
Stanford, CA 94305-5015
U.S.A.
E-mail: montgomery _ david@gsb.stanford.edu
Tel. +1 650 723-3029
Fax +1 650 725-9932
2
Professor of Strategic and International Management
London Business School
Regent’s Park
London NW1 4SA
ENGLAND
E-mail: gyip@london.edu
Tel. +44 20 7262 5050
Fax +44 20 7724 7875
3
Assistant Professor
Harvard Business School
Soldiers Field
Boston, MA 02163
U.S.A.
E-mail: bvillalonga@hbs.edu
Tel. +1 617 495 5061
Fax +1 617 496 5271
We would like to thank Peter M. Bentler, UCLA, for his comments on an earlier version of this paper, as well as the assistance of Javier Gomez Biscarri, UCLA, and Dana McLaurin,
Stanford. All remaining errors are, of course, our own. Financial support from the following institutions is also gratefully acknowledged: Marketing Science Institute, Stanford Graduate
School of Business, Center for International Business Education and Research at UCLA,
Fulbright Commission, Fundación Caja de Madrid, Fundación Ramón Areces (Spain), and
University of Cambridge.
ALL RIGHTS RESERVED. DO NOT COPY, TRANSMIT OR QUOTE WITHOUT
PERMISSION.
2

AN INDUSTRY EXPLANATION OF GLOBAL ACCOUNT MANAGEMENT
Abstract
Using globalization and contingency theory, this paper develops a model of global account management (GAM). The model comprises the multinational supplier’s industry globalization drivers, the multinational customers’ extent of globally coordinated buying, such customers’ demand for GAM services, the supplier’s response in terms of using various aspects of GAM, and resulting possible improvement in the supplier’s performance. The paper develops six hypotheses linking these variables. Data on various aspects of these variables were collected in a survey of 191 executives in multinational companies. Two related models are estimated from these data using a structural equations method. The results support the argument that the supplier’s industry globalization drivers play a key role in affecting customers’ demand for GAM services, and that supplier’s implementation of GAM leads to significant performance improvements. i

AN INDUSTRY EXPLANATION OF GLOBAL ACCOUNT MANAGEMENT
Multinational companies increasingly use a variety of management techniques for coordinating their activities with multinational customers. Different companies use different terms to refer to this coordination activity, such as “parent account management,” “international account management,” or “worldwide account management,” but the most common denomination for it seems to be global account management . We define g lobal account management as an organizational form and process in multinational companies by which the worldwide activities serving a given multinational customer are coordinated centrally by one person or team within the supplying company.
OBJECTIVE
Our objective is to develop and empirically test a conceptually grounded explanation of global account management. Global customer management involves multiple phenomena— internationalization and globalization, industry drivers, supplier-customer relationships, marketing strategies, and marketing organization and implementation. Hence, not surprisingly, several theoretical perspectives can be applied to analyzing and explaining this phenomenon— internationalization theory; globalization theory; relationship marketing; contingency theory applying to environment-strategy fit; organization-strategy fit and interorganizational fit; and resource dependency theory and bargaining power. Millman (1996) views GAM as a response to structural changes in the environment, i.e., a contingency perspective. Parvatiyar and Gruen
(1999) argue that, whether firms should implement GAM, is contingent on both the supplier’s and the customer’s stage of globalization. Nahapiet (1994) views GAM as a form of interorganizational fit. Arnold, Birkinshaw and Toulan (2001a) use resource dependency and information processing theories to analyze GAM. Furthermore, they argue that it is not the specific aspects of GAM use that matter so much as the breadth and intensity of the GAM relationship between customer and supplier.
At this early stage of theory building about a relatively new phenomenon, studies with alternative theoretical lenses can contribute greatly to overall understanding. Hence, as an alternative to the theories already applied, we develop a model that uses primarily globalization
1

theory, particularly from the industrial organization perspective of industry globalization drivers; and secondly contingency theory focusing on the fit between customer demands and supplier responses.
INTERNATIONAL CUSTOMER-SUPPLIER STRUCTURAL RELATIONSHIPS
Before developing our model, we need to first understand the different types of customersupplier structural relationships, of which there are many.
Alternative Structural Relationships
We define several types of international customer-supplier structural relationships, showing the purest forms, although hybrids are obviously possible. We also distinguish between critical and trivial purchased inputs. Our models apply to critical inputs (e.g., gearboxes for automakers and food for airlines) and how multinational companies are evolving in their buying behavior for such inputs. For trivial inputs (e.g., stationery and food for automakers, and cars for law firms), most companies have stayed with domestic or multilocal buying (defined below).
[Figure 1. International Customer-Supplier Structural Relationships] a.
Multilocal Buying
In Multilocal Buying (Figure 1a), each national unit of a customer buys from the matching local national units of a multinational supplier. No cross-national coordination is done by either company. This has, traditionally, been a very common form of buying relationship among multinational companies. Each national unit of a customer can also have relationships with other same country units of other suppliers, and vice versa for national units of suppliers.
b.
International Buying
In International Buying (Figure 1b), a unit (either a national subsidiary or a regional or global headquarters) of the customer company buys from one or a few foreign units of a supplier(s) (Figure 1). No cross-national internal coordination is done by the buyer. c. Multinational Buying and Selling
In Multinational Buying and Selling (Figure 1c), individual national units of the buying company purchase from a few domestic and foreign units of a supplier (upper half of Figure 1c).
Or individual country units of the supplier sell to several country units of the customer (lower half of Figure 1c). No (or only limited) cross-national coordination is done by either company. In
2

an example of multinational buying, the home country units of large American and European (but not Japanese) automotive manufacturers had until the 1990s searched the world for suppliers but to supply only their domestic operations. Foreign units were left to do their own purchasing. In contrast, most Japanese companies have long used globally coordinated buying, (structure E, below). Multinational selling has been a very common approach in companies where country units have significant autonomy and seek their own customers and markets. d. Globally Coordinated Buying
In Globally Coordinated Buying (Figure 1d), purchases for a given critical input are coordinated centrally by a global unit, usually but not necessarily located in the headquarters country and increasingly located in cyberspace as a virtual global buying team. Companies who buy this way constitute genuine global customers. This central unit then deals with individual country units of the supplier although individual customer country units may have partial relationships with corresponding supplier country units in the same country (dashed lines in
Figure 1d). e. Globally Coordinated Selling
In Globally Coordinated Selling (Figure 1e), the selling activities of a multinational supplier are coordinated centrally. This coordination can be done by some combination of a global unit, usually but not necessarily located in the supplier’s headquarters country, and by a global account manager usually located in the headquarters country of the customer company.
The customer itself may not be coordinating its purchases and so the supplier may be dealing directly with separate customer country units, as indicated by the arrows in Figure 1f. Individual supplier country units may have partial relationships with corresponding customer country units in the same country (dashed lines in Figure 1e). f.
Global Customer-Supplier Relationship
In Global Customer-Supplier Relationship, the structures of Globally Coordinated Buying and Globally Coordinated Selling are combined (Figure 1f). The primary relationship is now between global buying and selling units, and between each global unit and its own country units
(solid lines in Figure 1f). Secondary relationships can exist directly between buyer and seller units in the same country, and between the global buying unit and individual seller country units, and between the global selling unit and individual buyer country units (dashed lines in Figure 1f).
3

Implications of Structures for International, Global, and Relationship Marketing
Each structure above has different implications for international, global, and relationship marketing. Following now established practice (e.g., Douglas and Craig, 1989; Quelch and Hoff,
1986; Jain, 1989; Yip, 1992) we define international marketing to be concerned with marketing across one or more frontiers, and global marketing to be concerned with developing and implementing marketing strategies that have global applicability. Regional marketing can be defined in the same way as global marketing, but for regional applicability.
Structure A, Multilocal Buying has the fewest implications for international marketing.
Relationships are essentially domestic in each case although foreign products or services may be imported, foreign marketing campaigns may need to be adapted, and there may be some foreign marketing staff in particular country units dealing with other nationals. The critical success factor here is for the supplier’s country units to not be lulled into thinking that they are conducting a purely domestic relationship. Behind both buyer and seller units are foreign parent companies (in most country pairs) and perhaps an imported marketing mix that may need adaptation. The relationships are a straightforward buyer-seller dyad, with possible minor interventions from each entities parent (global or regional) organizations.
In Structure B, International Buying, the foreignness is clear to both sides. Classic issues of marketing adaptation apply. Managing a foreign relationship in the dyad becomes clearly necessary.
Structure C, Multinational Buying and Selling, also presents clearly foreign relationships and a possible need to adapt the marketing mix. Both buyer and seller units can be involved in multiple dyadic relationships. The danger here for the seller lies in its lack of coordination. In multinational buying, customer units may well play seller units against each other, getting rival bids from within the same selling company. Conversely, in multinational selling, suppliers may be able to pick and choose the customer units, to which they sell, depending on prices and terms.
Structure D, Globally Coordinated Buying, reduces the need for supplier units to maintain multiple, foreign relationships, or even to adapt the marketing mix. Typically, the seller units have a simple dyadic relationship with the global buying unit of the customer. This global unit mediates between the customer country units and the seller country units, usually reducing the need for adaptation. But this structure poses the greatest danger for the supplier. Instead of the buyer’s country units independently dealing with multiple units of the seller, it is now one
4

coordinated global unit doing the buying, creating great asymmetry of information and bargaining power.
Structure E, Globally Coordinated Selling, constitutes a partial and asymmetric form of global account management. The supplier is globally coordinated but not the customer. There are two possible roles for the global selling unit in relation to the customer’s country units. First, as there is no corresponding customer’s global buying unit, the supplier's global unit may be invisible to individual country units of the seller, working behind the scenes to coordinate the seller’s units who maintain direct relationships with buyer units in each country. Second, in a much more visible and stronger role, the supplier’s global unit may displace its country units and take over direct relationships with customer country units. In either case, the supplier now gets the benefit of asymmetry in information and bargaining power.
Structure F, Global Customer-Supplier Relationship, constitutes full global account management. The global units of both customers and suppliers have a direct dyadic relationship with each other. Both sides now focus on global rather than international marketing issues, as both seek primarily for globally common solutions, while minimizing local variances. At the same time, each global unit, especially that of the supplier, has to manage complex polyadic relationships both internally and externally.
Being a Global Customer or Supplier
From the above arguments we conclude that being a global customer occurs in only
Structures D and F, while being a global supplier occurs in only Structures E and F.In the case of global customers, the most macro view is that a significant proportion of their buying is done on a globally coordinated basis. For global suppliers, a significant proportion of their selling is done on a globally coordinated basis. In the complex world of globalization, companies can and do play multiple roles. So a customer can be a global one for some types of purchases and not others, and a supplier a global one for some types of sales and not others. So the larger the proportion of coordinated buying or selling, the more they are global customers or global suppliers. But these two definitions constitute only the top-level views. Beneath coordinated buying or selling are several other micro activities, such as demanding global pricing or global contracts in the case of global customers and providing global account staff or global contracts in the case of global suppliers. In developing our model of global account management we will include both these levels of the phenomenon.
5

A MODEL OF GAM
Our overall model of GAM is presented in Figure 2. Globalization drivers in the supplier’s industry affect the extent to which its customers buy on a global basis (“Customers’
Global Buying”), and also the specific types of GAM services that they demand. In addition, these supplier industry drivers also directly affect the supplier’s use of GAM. The extent to which customers buy on a global basis directly affects the types of GAM services demanded by customers, which in turn directly affects supplier’s use of GAM. Lastly, the supplier’s use of
GAM improves its own performance.
[Figure 2. Model of Global Account Management]
Industry Globalization Drivers
Implementing global customer management incurs significant costs for suppliers, especially those of coordination across national units. Similarly, buyers incur coordination costs and the like when they shift to globally integrated purchasing. Consequently, there needs to be strong business justifications for at least one side, preferably both. So while purely organizational factors can have an important effect on the decisions to adopt global purchasing or global account management, we look first to business motives. Possible business benefits to a customer include: consistency in service quality and performance, standardization of products and services, lower and more uniform prices, and uniform terms and conditions. Possible organization benefits include: having a single point of contact and mutual coordination of planning.
A major stream of globalization theory focuses on the industry or the nature of the business as the key determinant of globalization potential (Porter, 1986; Morrison and Roth 1992;
Yip 1992; and Johansson and Yip 1994; Malnight 1995). Another stream focuses more on the organization preparedness of individual firms (e.g., Bartlett and Ghoshal, 1989; Roth, Schweiger, and Morrison 1991; and Kim and Mauborgne 1995) Both should apply to global account management, with industry factors driving business benefits.
Whose industry is relevant? The customer's or the supplier’s? Given that customers buy many types of inputs, some critical and others trivial, the customer industry should not be the primary driver of the need for global account management. For example, an automobile manufacturer may need a global account relationship with the supplier of its gearboxes, but not
6

for the suppliers of its stationery. Hence it is the industry of the supplier that drives global account management needs.
Johansson and Yip (1994) defined and tested four categories of industry globalization drivers for global strategy in general: market, cost, government, and competitive drivers. For
GAM, market globalization drivers affect the extent to which a supplier’s industry meets globally common customer needs, hence affording the possibility of global products (goods and services).
In turn, the possibility of global products encourages multinational customers to buy the same products globally and to seek related global standardization in contracts, pricing, terms and conditions, service, etc. An example would be how the benefits of globally common process controls might induce an oil refining multinational (e.g., Shell) to act as a global customer in procuring such products from a multinational supplier (e.g., Honeywell).
Cost globalization drivers include the extent to which global scale in value adding activities drives down costs, and the extent to which favorable logistics allow global shipment of products. Scale-related cost reductions in supplier product development or production could be reaped by globally integrating development activities for a given multinational customer. For example, most automakers would want their suppliers of components to have global product development programs that are globally coordinated for their needs. Favorable logistics would also favor a global account relationship: the supplier could manage for the customer a global supply system that optimizes cost and other aspects of delivery. For example, FedEx manages
Laura Ashley as a global account, providing a globally integrated logistics system.
Government globalization drivers, such as tariff and non-tariff barriers, regulation, and investment rules affect the extent to which a supplier can run a globally integrated operation, particularly in cross-border movements of goods but also the meeting of local rules. For example, in highly regulated industries, such as pharmaceuticals, there are almost no international, let alone global, customers (with the minor exception of the World Health Organization). In partially regulated industries such as postal and telecommunication services, there are many potential global customers (most multinationals) but nationally restricted suppliers have to struggle to create cross-border alliances (e.g., AT&T’s and BT’s failed Concert alliance) to serve them.
Transferable competitive advantage is the competitive globalization driver most relevant to GAM. For example, most technology advantages are easily transferable globally, while at the other extreme, most relationship advantages or advantages based on local knowledge are much
7

harder to transfer. Where the advantages are transferable, customers will seek the globally best solutions to enhance their own competitiveness. Hence, multinational customers for computer products increasingly seek to procure these on a global basis. In contrast, multinational users of legal services still tend procure almost entirely on a local basis. Customers for advertising services are somewhere in between but increasing numbers require GAM relationships with a small number of global agencies.
Supplier industry globalization drivers should have two effects. At a macro level, they should be a strong determinant of the overall degree to which a customer buys on a globally coordinated basis.
H1: Globalization drivers in a multinational supplier’s industry constitute direct determinants of the extent to which its multinational customers buy on a globally coordinated basis.
At a micro level, industry globalization drivers should be a strong determinant of the types of global account management services that the customer demands. For example, a supplier industry that has a strong driver for globally compatible technical standards (e.g., the computer industry) will induce its customers to particularly demand globally standardized products. Or a supplier industry with a strong driver of cost-effective transportation (e.g., high value-to-weight items such as electronic components) will induce its customers to particularly demand globally standardized pricing. In contrast, lack of transportability (as in the case of international express package services) prevents customers from truly pressing suppliers for global pricing (which
DHL, for example, is mostly able to avoid).
H2: Globalization drivers in a multinational supplier’s industry constitute direct determinants of its multinational customers’ demands for specific aspects of global account management.
Thirdly, globalization drivers in the supplier’s industry should also directly affect the supplier’s use of GAM, regardless of customer behavior. Thus being in a global or globalizing industry should make suppliers see the potential for offering GAM services, whether or not their customers are demanding it. Obviously, customer demand for GAM will reinforce the industry effect. But proactive suppliers should respond directly, even if only partially, to globalization drivers of their own industries.
8

H3: Globalization drivers in a multinational supplier’s industry constitute direct determinants of the supplier’s use of global account management.
Customers’ Global Buying
As discussed earlier, the extent to which customers buy on a globally coordinated basis constitutes the first level measure of whether they are global customers (Structure D. Globally
Coordinated Buying. The more they are global customers, the more aspects of GAM will they demand from suppliers in order to support their global buying behavior. This is an effect of both scale and complexity. A customer with only a low percentage of globally coordinated purchases
(for a given type of purchase) can perhaps do most of the coordination itself. As this percentage increases, the customer will need more support from its supplier.
H4: The more that a multinational customer buys on a globally coordinated basis, the greater will be the extent to which it demands aspects of GAM from its multinational
suppliers.
Customers’ Demand for GAM
What aspects of global account management do customers demand? The previous research on GAM (Nahapiet 1994, Millman 1996, and Yip and Madsen 1996), and a series of interviews (described later) conducted for this study, allowed us to identify the following list of possible customers’ requests for aspects of global account management:
* Single point of contact . Globalized customers need a single point of contact within each supplier. This single point then enables better negotiation and management of the relationship.
* Coordination of resources for serving customers . Globalized customers also require better coordination of their suppliers’ resources for serving them. Such needs for coordination include meshing of the supplier’s global activity network with that of the customer. For example, “just-in-time” production is now practiced on a global basis, placing high demands on customer-supplier coordination.
* Uniform prices . Globalized customers seek to avoid paying different prices in different countries unless there is cost justification (e.g., transportation, order size, special versions) rather than just market variations (i.e., prices are higher in some markets than others because of supply and demand or historical reasons). Essentially, globalized customers seek globally uniform prices and require an acceptable justification for any deviations.
9

* Uniform terms of trade . Globalized customers also seek uniformity in all terms of trade, and not just price. So they increasingly demand uniformity in such issues as volume discounts, transportation charges, overhead, special charges, etc.
* Standardization of products and services . Globalized customers increasingly seek to themselves produce standardized products and services and in turn need standardized supplies. Also, companies with global strategies increasingly seek to develop globally integrated organizations and management processes. In turn they expect standardized products and services in support of their organization and management processes, particularly in the case of productivity tools such as computing and communication products and services.
* Consistency in service quality and performance . Globalized customers seek a high degree of standardization and consistency in their own global operations. Accordingly, they need their suppliers to provide corresponding consistency in service quality and performance. For example, a global airline needs consistency in its suppliers, whether of maintenance or catering services; or a global manufacturer needs consistent servicing of its machinery.
* Service in markets in which the supplier has no customer operations . Globalized customers often operate in more geographies than do their suppliers. Typically, the more geographically spread MNCs are more likely to demand global account management services. A particularly tough requirement is for the supplier to serve the customer in a geography where the supplier does not have operations. A truly responsive supplier would set up operations in the new geography or else face the threat of losing risking the entire global relationship by allowing a competitor to serve the customer in that geography.
Interestingly, most of this list of demands concerns essentially end results, such as uniform prices. Only the first two items, “coordination of resources for serving customers” and
“single point of contact” concern process, and these are almost an end result in that they directly save time and effort for the customer.
Response to Demand for GAM
Contingency theory (e.g., Lawrence and Lorsch 1967; Miles, Snow and Pfeffer, 1974), standard microeconomic supply and demand theory, and relationship marketing theory (e.g.,
Jaworski and Kohli 1993, Kalwani and Narayandas 1995, and Grönroos, 1997), would all argue that most suppliers will seek to respond to their customer’s demands for GAM. Even so, there is evidence that some suppliers resist GAM because of their fear that its adoption will help the
10

customer to demand lower worldwide prices. For example, Yip and Madsen (1996) cite Xerox as denying customers’ requests for GAM if the request is motivated only by a desire to pay uniform prices worldwide. Furthermore, Nahapiet (1994) argues that GAM is an onerous activity for suppliers. Hence, it is not axiomatic that suppliers will fully respond to customer requests for
GAM. Nevertheless, most suppliers are likely to provide some GAM services demanded by their customers.
H5: The greater the extent to which its multinational customers demand GAM the greater the extent to which it is implemented within a multinational supplier.
Supplier Use of GAM
GAM is an extensive form of intervention in the organization and involves all of the latter’s major categories, such as organization structure, management processes, people, and culture (commonly used in the international literature, e.g., Prahalad and Doz (1986). Based on the previous literature and on the interview phase of this study, we identified eight key aspects of the extent of GAM use, in terms of the existence on a global basis of:
* Global account managers.
Perhaps the single most important way to implement GAM is to designate a global account manager with dedicated responsibility for a global account.
Typically managers are located in the customer’s headquarters’ country.
* Support staff. A global account manager cannot operate alone but requires support staff. For example, Hewlett-Packard’s GAM program provides for support staff at H-P’s own headquarters while the global account manager is based near the customer’s headquarters.
* Revenue or profit measures. Evaluating and compensating global account personnel depends on knowing the performance of global accounts, particularly revenues and profits on a global rather than national or regional basis. The creation of such global performance measures is a difficult yet very necessary aspect of implementing GAM.
* Reporting processes. More generally, a GAM program needs to have reporting processes on all aspects of a global account, not just on revenues and profits but also on customer satisfaction, wins and losses, and use of global account services in different geographies.
* Customer information. An effective GAM reporting process will result in extensive information about the customer globally, and provide a basis for improving performance for both the customer and the supplier. Furthermore, an effective GAM program provides for the central collation of previously dispersed or uncollected customer information.
11

* Personnel evaluation. Managers directly involved in GAM programs, as designated global account managers or staff, need to be evaluated on a global rather than just national or regional basis. In addition, managers indirectly involved, such as country managers and sales personnel, need to have a global customer component added to their primarily national or regional evaluation basis. Changing evaluation systems is known to be highly difficult.
* Incentives and compensation. Changing the evaluation system has the objective of changing and rewarding behavior. Incentives and compensation provide some of the most powerful influences on managers’ behavior, particularly in sales situations. Much previous research has shown how country managers have failed to change their behavior to support global strategies when their compensation continues to be set on a national basis. But changing the compensation system turns out to be one of the most difficult challenges for globalizing companies (Johansson and Yip, 1994).
* Customer councils or panels. Lastly, a GAM program is very much a dyadic relationship, requiring extensive and continuing feedback from customers to suppliers and vice versa.
Companies may implement customer panels or councils as part of their GAM program.
In contrast to the list of customer demands, this list of supplier use covers entirely process items rather than content. So customer demand by certain types of GAM content is met by suppliers implementing certain types of GAM process (e.g., global account managers), which in turn delivers the desired content (e.g., standardized products). Hence it seems essential for suppliers to provide the right process and not just respond directly to content demands.
Use by suppliers of some or all aspects of GAM constitute our earlier identified Structure
E, Globally Coordinated Selling. The combination of customers’ global buying and supplier’s use of GAM represents Structure F, Global Customer-Supplier Relationship (H4 and H5 in the model).
Performance Effects
Contingency theory also suggests that a strategy (using GAM) that matches the environment (demand for GAM) should yield superior performance for the supplier. There has been no evidence to date on the performance consequences of using GAM.
In terms of GAM effects on performance, Nahapiet (1994) argues that the use of GAM should create value through coordination above and beyond the incremental costs. Accordingly,
12

we propose that the use of GAM should have favorable effects on company performance in terms of customer satisfaction, retention, and gain; and company revenues and profits.
H6: The greater the extent to which a multinational supplier’s global account management program responds to customers’ demands for it, the more favorable the
effect on the supplier’s performance.
In summary, we have developed six hypotheses that define the paths in our model as depicted in Figure 2. Three of these hypotheses and paths derive from industry globalization drivers (H1, H2 and H3) and three from contingency theory (H4, H5 and H6).
METHODOLOGY
Ideally, a study of GAM should include responses from both customer and suppliers. But given the great difficulty of getting cooperation from customers, we have opted to collect data from suppliers. While recognizing the limitations of such an approach, the suppliers are the ones who have to make the most effort in GAM. Hence it is legitimate to focus on their views. So we developed a questionnaire for suppliers to complete about their customers’ demand for GAM and their own use of it. We had this questionnaire completed by 191 senior international executives in multinational supplier companies. To obtain data on industry globalization drivers we used an expert panel of three Ph.D. students in international business. In data analysis we specified and tested two structural equation models.
Questionnaire Design
We developed the supplier questionnaire through an iterative series of interviews and pretests in the United States with seven senior international executives from the following companies: Andersen Consulting, AT&T, Hewlett-Packard, MasterCard, McKinsey, Price
Waterhouse, and World Partners (an alliance between AT&T and overseas partners to provide global telecommunications services). We repeated this pre-testing and subsequent modification process three times before mailing the final version. The questionnaire had three parts, covering: customers’ demand, extent of use of GAM programs, and performance effects. Most of the items used a seven-point Likert-type scale. In addition, the initial section about the respondent included information about the degree of globality of both the company and its customers.
13

Sample
The population frame for this study consists of multinational supplier companies that also have multinational customers (whether or not they buy on a globally coordinated basis). Our sample of 191 responses comes from four different sources: one mailed survey and three convenience samples from senior-level executive education programs conducted at two major
U.S. business schools. In all four samples, respondents were nearly all at the level of vice president or higher. The specific sources are the following:
1) Mail Survey : A survey was mailed to heads of international operations or CEOs in 800 U.S. companies from the Directory of U.S. Firms Operating in Foreign Countries . These firms all had significant global operations, which we defined as being present in at least three continents
(including North America) and had more than one thousand employees. We received 57 useable responses.
2) Senior Executive Program : Participants in the program, who represented companies from all over the world, were asked to complete the survey. From the responses that were received, we selected 68 useable responses from companies with significant global operations.
3) Marketing Management Program : Participants in a different program responded, of which 36 represented companies with significant global operations.
4) Advanced Executive Program : Participants responded with 30 useable responses from companies with significant global operations.
The mail survey had a very low response rate of 7%. But the convenience samples from the three executive programs all had response rates of over 90% as the participants were asked to complete the surveys while they were on site in the business school programs. This difference in response rates may raise a concern about whether the data are different across the two types of samples. We address this issue below by testing and discussing whether these four samples can be pooled.
All 191 supplier responses reported on customers’ demands for global account management, hence satisfying our population frame criterion that they have multinational customers. However, one of these responses had no information about the supplier’s industry, and hence had to be dropped from the sample when testing our industry-drivers-based models of
GAM. Of the remaining 190 usable responses, only 135, or 71%, reported some use of GAM.
Furthermore, only 13% of the total revenues of these 190 came from customers buying on a
14

globally coordinated basis. We can interpret these data as showing that a large proportion (71%) of those multinational suppliers with multinational customers provide some form of GAM, but that these GAM services are aimed primarily at customers who account for a small proportion
(13%) of total revenues. In addition, 26% of total supplier revenues come from all multinational customers, i.e., half of all multinational customer revenues came from globally coordinated purchasing. Lastly, 46% of total supplier revenues come from all international customers, showing that 20% (46% minus 26%) come from foreign, but non-multinational, customers.
The respondent multinational companies come from 33 different countries spread across all the regions of the world: North, South, and Central America; Western and Eastern Europe;
East Asia; Africa; and Oceania. On average, the companies have operations in four of these regions. U.S. companies make up 70% (133 out of 191) of the sample. Median company revenues are $1,956 million.
Pooling Tests
In order to assess the appropriateness of pooling all the subsamples for the estimation of the structural models, we carried out, for each of the ten survey-derived variables in the models, one-way analysis of variance (ANOVA) across the four subsamples. The results of these tests are shown in the first three columns of Table 1. As the third column of Table 1 indicates, none of the
F-statistics was significant. Therefore, we cannot reject the null hypothesis of no differences across subsamples. These results were confirmed by t-tests of differences between each of the four subsamples and the pool of the other three, for each variable. The t-statistics from these tests appear in the next four columns of the same table (i.e., columns 4 to 7 of Table 2). Of these 40 tests (4 subsamples times 10 variables), only one was significant: the one concerning the difference in GAM use (at all), first, between the mailed survey group of respondents and the pool of the other three subsamples (t = 2.78); and, second, between the U.S. and non-U.S. samples (t = 3.07). So we can be reasonably confident that all our subsamples are drawn from a common population, and can be pooled.
[Table 1. Pooling Tests]
Data on Industry Globalization Drivers
The respondents reported on the identity of their own (supplier) industries but did not report on their industries’ globalization drivers. To obtain these data we had three Ph.D. students from a major British business school estimate the drivers for each respondent company's industry.
15

All three raters had been through a common course of study on global strategy and were provided with a definition of four globalization drivers (based on Yip, 1992): market, cost, government, and competitive. The students had no knowledge of the data provided by the respondents other than the identity of the industry and the name of the respondent’s company. One rater was
British, one German and one Latin American. To adjust for inter-rater differences, we compared the overall mean scores on all drivers for all industries. The British and the German raters produced very similar means of 3.90 and 3.82 (average of 3.86), while the Latin American rater produced a mean of 4.98. So we adjusted the scores of the Latin American rater by a factor of
0.78 (= 3.86/4.98).
Alternative Structural Models
We estimate and test the validity of our model using structural equations modeling with the EQS software package (Bentler 1995; Bentler and Wu, 1995). Because using GAM at all is a central focus of our study, we estimate one model where overall GAM use is the end, dependent
(dummy) variable; and another model with detailed aspects of GAM use and an end dependent variable of performance.
Variables and Measurements
All of the variables we used, unless otherwise indicated below, were measured on a seven-point Likert scale with “very low” and “very high” as the end point descriptors. For all multi-variable indicators or constructs, we computed their Cronbach’s alpha as a measure of their internal consistency.
1 All of them are above 0.70, thus satisfying Nunnally’s (1978) minimum criterion for internal consistency.
Supplier’s Industry Globalization Drivers.
Latent variable measured by four indicators, one for each driver (market, cost, government, and competitive). Alpha = 0.91.
Customers’ Extent of Global Buying
. We used the percentage of company revenues accounted for by coordinated multinational customers (i.e. those multinational customers who coordinate purchases across countries). The percentage figure has been divided by 10 to yield a measure within a value range more similar to the rest of the variables.
Customers’ Demand for GAM . The survey measured seven aspects of customers’ demand for GAM. We grouped six of these items into three different composites, which is a common practice in structural modeling when the number of original indicators is larger than three or four
(Baumgartner and Homburg 1996).
Hence, we operationalize customers’ demand as a latent
16

variable measured by three indicators, which in turn result from aggregating two or three questionnaire items of related meaning:
Coordination : This variable averages three of the survey measures on the extent to which multinational customers request: (1) GAM overall, (2) greater global coordination and integration of resources for serving customers, and (3) a single point of contact. Alpha =
0.80.
Uniform trade terms : This variable averages two of the survey measures on the extent of requests for: (1) more uniform prices charged to them in the different countries in which the company serves them and, (2) more uniform terms of trade other than price. Alpha =
0.88.
Consistent service : This variable averages two of the survey measures on the extent of requests for: (1) greater standardization across countries in products or services, and (2) more consistency in service quality and performance. Alpha = 0.83.
The Cronbach’s alpha of the composite of these three indicators is 0.80.
Supplier’s GAM Use-At All
. Dummy variable = 1 when respondents reported use of any aspect of GAM.
Supplier’s Use of GAM-Aspects.
Latent variable measured by four indicators, each in turn being the average of two measures:
1) Manager/Team : (1) managers, directors, or similar positions responsible for a global account, and
(2) support staff or team for the global account. Alpha = 0.86.
2) Customer involvement : (1) customer information about the global account, and (2) customer councils or panels. Alpha = 0.71.
3) Performance evaluation/control : (1) revenue/profit measures for the global account, and (2) reporting processes for the global account. Alpha = 0.90.
4) Personnel evaluation/compensation : (1) evaluation of the personnel involved, and (2) global personnel incentives and compensation. Alpha = 0.73.
The Cronbach’s alpha of the composite of these four indicators is 0.88.
Supplier’s Performance . Respondent’s evaluation of what has been the overall effect of their company’s GAM programs. We also aimed for a more comprehensive measure of performance in the questionnaire, where we asked about the approximate percentage
17

improvement in performance over the last five years attributable to GAM programs in terms of
(1) customer satisfaction, (2) revenues, and (3) profits. But the extent of missing responses for these three items led us to use the single, overall indicator of performance in our analysis.
RESULTS
The results support the overall model formulation and that supplier industry globalization drivers are a primary determinant of customer demand for and supplier use of GAM. All hypotheses are supported except for Hypothesis 3 (that the supplier’s industry globalization drivers directly affect the supplier’s use of GAM.)
Measurement Model
Applying Andersen and Gerbing’s (1988) two-step approach, we first estimated the measurement model, then the full model. The results for the measurement models are shown in
Figures 3 and 4 as the paths from the variables (ellipses) to the indicators (rectangles). Most indicator loadings are very high, with six of seven above .70, and one at .68 for Model 1; and nine of eleven above .70 , one at .68, and one at .57 for Model 2.
[Figure 3. Model 1: Supplier GAM Use (At All)]
[Figure 4. Model 2: Supplier GAM Use (Aspects) and Supplier’s Performance]
Structural Paths
In Model 1 (Figure 3, with Supplier’s GAM Use as a dummy variable only and without a performance dependent variable), the following direct structural path coefficients (all standardized
2 ) are significant at .01 or better: .20 from Supplier’s Industry Globalization to
Customers’ Global Buying (H1), .23 from Supplier’s Industry Globalization to Customers’ GAM
Demand (H2), .34 from Customers’ Global Buying to Customers’ GAM Demand (H4), and .26 from Customers’ GAM Demand to Supplier’s GAM Use (H5). Only the path of .08 from
Supplier’s Industry Globalization to Supplier’s GAM Use (H3) is not significant. In Model 2
(Figure 4, with Supplier’s GAM Use more fully specified with various indicators, and with an added dependent variable of Supplier’s Performance), most paths stay of the same magnitude and significance: the H1 path changes from .20 to .16, the H2 path from .23 to .26, the H3 path from
.08 to ,11 (and still not significant), and the H4 path from .34 to .27. But the H5 path jumps from
.26 to .60, which is not surprising given that Model 2 uses the more fully specified version of
18

Supplier GAM use. In addition, Model 2 has a very large and significant path coefficient of .77 from Supplier’s GAM Use to Supplier’s Performance Improvement.
Goodness of Fit
The models also score well on various measures of goodness of fit. First, they achieve high explained variance for two of the dependent variables Supplier’s GAM Use-Aspects (.41 in
Model 2) and Supplier’s Performance Improvement (.60), and a moderate level for Customers’
GAM Demand (.20 in Model 1 and .16 in Model 2) (Table 2). As indicated in Figures 3 and 4,
Models 1 and 2 resulted in p-values of .23 and .13 respectively, which is indicative of a good fit.
The three other goodness-of-fit indices considered (Bentler-Bonett Normed and Non-Normed, and Comparative Fit Index), which range between 0.91 and 0.99, also suggest a good fit under their respective criteria.
[Table 2. Comparison of Explained Variance]
Comparison of Explained Variance
Table 2 also shows that the best fit occurs for Supplier’s Performance as a function of
Supplier’s GAM Use-Aspects in Model 2 ( R 2
= 0.60). In an absolute sense this is an excellent fit for a cross-sectional data (even allowing for possible common methods bias in the use of one rater for both variables). The equation in Model 2 for Supplier GAM Use-Aspects as a function of Customer’s GAM Demand and Supplier’s Industry Globalization is also an excellent fit (R 2 =
0.41) . However, the fit for Supplier GAM Use-At All in Model 1 was much more weakly related to these same independent variables ( R 2 = 0.09) . Lastly, Customer’s GAM Demand is quite well fitted by Supplier’s Industry Globalization and Customers’ Global Buying (R 2
= 0.20 and 0.16 for Models 1 and 2 respectively). These fits are good by the standards of cross-sectional data analysis.
Structural Relationships
To look at the significance of effects, we also calculated the direct and indirect effects of each independent variable on each dependent variable, in each model, using the maximum likelihood estimates (MLE) of the unstandardized path coefficients, reported in Tables 3 and 4
(note that these unstandardized coefficients will not be the same as the standardized coefficients in Figures 3 and 4) 3 . As usual, the indirect effects are calculated by multiplying the paths via intervening variables, then summing where more than one indirect path.
[Table 3. Model 1 Direct and Indirect MLE Unstandardized Estimates]
19

[Table 4. Model 2 Direct and Indirect MLE Unstandardized Estimates]
For Model 1 (Table 3), the direct effects estimates are all positive, as expected in the hypotheses. Four of five direct paths predicted in the hypotheses ( H1, H2, H4, and H5) were all significant at p < 0.01 or beyond. H3 received very weak or marginal support with p <0.15. Note that the indirect effects are mediated by other variables in a model which themselves have direct paths to the dependent variable. In Model 1 all three indirect effects were significant at p < 0.02 or beyond and with the anticipated sign. These significant indirect effects support the path model proposed with indirect effects. In sum, in Model 1 there was substantial support for all hypotheses ( direct effects), with the exception of H3, which was weakly supported, at best. The significant indirect effects also add support to our proposed model.
For Model 2 (Table 4), all coefficients have the expected positive sign. Four of six direct paths ( H2, H4, H5, and H6) are significant at the p< 0.005 level and beyond. The support for H1 in this model is weaker, p<0.072, than in Model 1 and the statistical support for H3 remains very weak at p < 0.125. In sum, all hypotheses received some support, and results are very strong for
H2, H4, H5, and H6. The results for H1 are weaker, while for H3 they are marginal. Recall that our hypotheses relate to internationalization theory (embodied in the supplier’s industry globalization drivers) via H1,H2, and H3; and to contingency theory (via the fit between customer demands and supplier responses) via H4, H5, and H6. Thus aspects of both theoretical perspectives received strong support.
Strength of Effects
To understand the relative strength of the various effects, we now look at Table 5, which presents the total, direct and indirect standardized effects (the direct effects matching the coefficients in Figures 3 and 4, except for rounding).
[Table 5. Total, Direct, and Indirect Standardized Effects]
Customer GAM Demand is roughly equally affected by Supplier’s Industry Globalization and Customers’ Global Buying in each of the models ( .297 versus .342 for Model 1 and .303 versus 0.261 for Model 2).
For
Supplier’s GAM Use-At All
in Model 1, Customers’ GAM Demand is the most important ( .261). Supplier’s Industry Globalization is second (.158) with about 60% of the impact of Customers’ GAM Demand on Supplier’s GAM Use-At All, while Customers’ Global
Buying has the least impact (.089). Note that both Supplier’s Industry Globalization and
20

Customers’ Global Buying have indirect effects mediated by their direct effects on Customers’
GAM Demand and its subsequent direct effect on Supplier’s GAM Use-At All. Lastly, Supplier’s
Industry Globalization’s direct effect on Supplier’s GAM Use ( .081) is roughly the same magnitude as its indirect effect ( .077) via Customers’ GAM Demand.
For
Supplier’s GAM Use-Extent
in Model 2, as with Supplier’s GAM Use-At All in
Model 1, Customers’ GAM Demand has the most powerful effect (.598) but more than double its leading effect on Supplier’s GAM Use-At All in Model 1. Supplier’s Industry Globalization is again the second most important variable with a level of .292 or about half that of Customers’
GAM Demand. Again , Customers’ Global Buying is the third most important (.161) at about half the effect of Supplier’s Industry Globalization.
Finally,
Supplier’s Performance
is strongly affected by Supplier’s GAM Use-Extent
(.77). Since this variable mediates the other three, the effects of all the others are indirect. It is interesting to note that the relative effect of the indirect variables of Supplier’s Industry
Globalization, Customers Global Buying, and Customers’ GAM Demand are essentially the same for Supplier’s Performance as for Supplier’s GAM Use-Extent, but at a somewhat lower level across the board.
DISCUSSION AND CONCLUSIONS
The results that all direct paths are statistically significant, except for that from Supplier’s
Industry Globalization to Supplier’s GAM Use (both At All and Aspects), implies that all the proposed hypotheses are statistically supported except for Hypothesis 3.
Effects of Supplier’s Industry Globalization Drivers
This is the first study to include industry globalization drivers as an explanation of GAM.
Our results show that the supplier’s industry globalization has strong direct effects on whether customers buy on a global basis and on whether they demand global account management from their suppliers. But the supplier’s industry globalization drivers do not directly influence the supplier to use global account management, only directly when customers globalize their buying and start to demand GAM services. Hence this finding confirms that suppliers tend to behave rationally, providing GAM services only in response to customers and not just because of their own industry characteristics. Lastly, we see that suppliers’ use of GAM has a strong effect on
21

improving Supplier’s Performance. Thus, our results provide support for our theoretical perspective that the supplier’s industry globalization drivers play a major role in explaining the
GAM phenomenon. Our positive results for industry globalization drivers further supports the stream of research on the role of industry in global strategy, and even its role in strategy in general. Industry does matter (cf. Rumelt 1991).
Effects of Customers’ Global Buying
The significant direct and indirect effects for Customers’ Global Buying shows that a key trigger for suppliers’ provision of GAM is that their multinational customers start to make some purchases on a globally coordinated basis. In particular, customers do not demand GAM services unless they themselves are a making significant percentage of their purchases (for a given product or service) on a globally coordinated basis.
Effects of Customers’ GAM Demand
Customers’ GAM Demand has a very large direct effect on Supplier’s GAM Use. So our survey and analyses support the qualitative and anecdotal evidence that most suppliers hold back on providing GAM services until their customers start to demand it. But an interesting distinction between Models 1 and 2 is that the path from Customers’ GAM Demand to Supplier’s GAM
Use-At All is much weaker at .26 than the path to Supplier’s GAM Use -Aspects at .60.
Similarly, the explanation of GAM Use-At All has the lowest R 2 of all five structural equations reported in the last column of Table 2 (0.09), while the explanation of Supplier’s GAM Use-
Aspects has the second largest R 2 at .41.
We conjecture that there may be a certain threshold effect. That is, it seems to take time for companies to start seeing to their customers’ requests for GAM (see also Montgomery and
Yip, 2000). Once they begin to do so, however, they respond effectively to this type of request.
Such behavior on the part of companies would be consistent with the existence of high setup costs for GAM programs, including barriers from organizational resistance. Once such costs have been incurred, or the initial resistance overcome, strengthening the different features of these programs, or extending its application to other customers would be relatively much less costly.
Determining whether this conjecture is actually the explanation to our findings represents a future research opportunity. These findings also provide further support for contingency theory in general and relationship marketing in particular.
22

Effects of Supplier’s GAM Use
The findings on the positive effect of the supplier’s use of GAM on its own performance supports two previous arguments. Nahapiet (1994 and 1998) argued that GAM involves a complex structure and process to build value for clients over time, and is an investment for the supplier. Our positive finding for performance shows a return on this supplier investment. Arnold et al (2001a), found that it is the intensity of the GAM relationship rather than specific aspects of
GAM that result in satisfactory performance of the GAM program. Our positive path for performance is from our construct, Supplier’s Use of GAM (Aspects), which is a composite of several individual aspects, providing indicative support for Arnold et al.
Our finding on performance suggests that this effect is strongly related to the extent to which the company has implemented the different features of these programs. While this may seem like an unsurprising finding, it is important to note that the implementation of a GAM program entails an organizational change and, as such, may encounter the resistance of some of its members. The experience of Citibank in the 1980s illustrates how such resistance may threaten the viability of a GAM program and, hence, lead to the waste of resources that had been invested, and to the damage of relationships with customers (Yip and Madsen 1996). Since the issue of obtaining net benefits is not so straightforward, it is encouraging to find that the overall balance from the use of GAM programs by the companies in our sample has been positive.
Implications for Theory
We mentioned at the start of this paper several different theoretical perspectives that could be applied to GAM: internationalization theory; globalization theory; contingency theory applying to environment-strategy fit, organization-strategy fit, and interorganizational fit; resource dependency theory, and bargaining power. Our exposition of alternative customersupplier structural relationships has, we hope, contributed to internationalization theory. Our model has directly used and tested globalization theory, particularly in regard to industry globalization drivers. Our examination of the linkage from customer demand to supplier use has tested contingency theories of fit. Furthermore, our construct, Customers’ Global Buying, is a direct indicator of customer bargaining power. Overall, we can conclude that our study has contributed a new but complementary perspective to extant ones on GAM.
23

Implications for Managers
This study indicates several areas for managers to consider. For managers in multinational suppliers, they need to understand the extent to which their own industry is globalized, because this is the first trigger for customers to globalize their buying behavior. Next, supplier managers need to closely monitor the extent to which customers are buying on a globally coordinated basis, not just for the supplier’s own category but also other categories bought by the customer. Third, supplier managers need to carefully understand which specific aspects of GAM are being demanded by customers. Which aspects are more important for this particular customer? Is it consistency in service? Or standardization of products? and so on. Fourth, supplier managers need to choose which individual aspects of GAM to implement and to what extent. One challenge is that successful programs need both corporate wide consistency and adaptation for individual customers. Furthermore, the most effective type of GAM program is likely to evolve over time both for the supplier as a whole and for its relationships with different customers. Fifth, using
GAM is likely to generate benefits for suppliers to offset the costs of such programs. But as usual, suppliers need to pursue a portfolio of different benefits, including at least enhanced customer satisfaction, revenues, and profits.
This study also has implications for managers in multinational customers. Most important, they can directly influence whether their multinational suppliers provide GAM services, and which particular aspects. They should also demand a share of the benefits that accrue the relationship.
Future Research
Our study suggests further research in at least three directions. First, studies can be conducted on both sides of the customer-supplier dyad. Do customers view GAM programs in the same way? Second, future studies can explicitly collect data from multiple levels in both customer and supplier organizations, distinguishing particularly among corporate level, division or business level, and country level managers. Third, differences by company nationality should be examined, particularly as among American, European and Asian companies.
24

FIGURE 1
International Customer -Supplier Structural Relationships
CUSTOMER SUPPLIER
CUSTOMER SUPPLIER
Country A
Country B
Country C
Country D
Country E
Country A
Country B
Country C
Country D
Country E
CUSTOMER
Country A
Country B
Country C
Country D
Country E
SUPPLIER
Country A
Country B
Country C
Country D
Country E
Country A
Country B
Country C
Country D
Country E
CUSTOMER
Country A
Country B
Country C
Country D
Country E
GU
Country A
Country B
Country C
Country D
Country E
SUPPLIER
Country A
Country B
Country C
Country D
Country E
CUSTOMER SUPPLIER SUPPLIER
Country A
Country B
Country C GU
Country A
Country B
Country C
CUSTOMER
Country A
Country B
Country C
GU GU
Country A
Country B
Country C
Country D Country D Country D Country D
Country E Country E Country E Country E
* Note: GU = global unit 25

Supplier's
Industry
Globalization
Drivers
H1
FIGURE 2
Model of Global Account Management
Customers'
Extent of
Global
Buying
H4
Customers'
Demand for GAM
H5
Supplier's
Use of
GAM
H2
H3
H6
Supplier
Performance
26

E1
E2
E3
E4
E6
E7
E8
0.46
0.42
0.68
0.50
0.43
0.73
0.65
Market
FIGURE 3
Model 1: Supplier’s GAM Use-At All
0.89
Cost
Government
0.91*
0.73*
0.87*
Supplier’s
Industry
Globalization
0.20*(H1)
Competitive
0.23*(H2 )
Customers’
Global Buying
Coordination
Uniform
Trade Terms
Consistent
Service
0.34* (H4)
0.90
0.68*
0.76*
Customers’
GAM Demand
0.26* ( H5 )
0.89
0.98
D1
E5
0.08
* (H3)
Supplier’s
GAM Use
-At All 0.96
E9
N = 190
CHI-SQUARE = 29.77 (P-VALUE = 0.23)
BENTLER-BONETT NORMED FIT INDEX= 0.96
BENTLER-BONETT NONNORMED FIT INDEX= 0.99
COMPARATIVE FIT INDEX (CFI) = 0.99
NOTE: Asterisks indicate whether the parameter was free (*) or fixed for estimating the model.
Coefficients are for the standardized solution.
27

FIGURE 4
Model 2: Supplier’s GAM Use-Aspects and Supplier’s Performance
E1
E2
E3
E4
E6
E7
E8
0.42
0.41
0.60
0.43
0
0.23
0.82
0.70
Market
Cost
Government
Competitive
Coordination
Uniform
Trade Terms
Consistent
Service
0.91
0.91*
0.80*
0.90*
0.97
Supplier’s
Industry
Globalization
0.16* (H1)
0.26* (H2)
Customers’
Globalization
0.27* ( H4 )
0.57*
Customers’
GAM Demand
0.91
0.72*
0.99
D1
E5
0.11
*
(H3)
0.60* ( H5 )
E9 0.50
Manager/
Team
Perf. Eval./
Reporting
0.87
E10
E11
0.55
0.73
Customer
Involvement
0.83*
0.68*
0.87*
Supplier’s
GAM Use
-Aspects
0.77
D2
E12
0.49
Personnel
Eval./Comp.
0.77* ( H6 )
N = 135
Supplier’s
Performance
0.64
CHI-SQUARE = 74.94 (P- VALUE = 0.13)
BENTLER-BONETT NORMED FIT INDEX= 0.91
BENTLER-BONETT NONNORMED FIT INDEX= 0.98
COMPARATIVE FIT INDEX (CFI) = 0.98
NOTE: Asterisks indicate whether the parameter was free (*) or fixed for estimating the model.
E13
Coefficients are for the standardized solution.
28

TABLE 1
Pooling Tests
Variables in Structural
Models
ANOVA
w = within groups d.f. a
F
(3,w)
p-val
169 1.09 0.35
t - statistics b, c
Mail vs. others
SEP vs. others
MMP vs. others
vs. nonothers US d
0.15 1.55 -0.93 -1.17 1.94 1
Customers’ Global
Buying
2 Coordination
3 Uniform Trade Terms
171 0.24
175 0.08
0.87
0.97
-0.73
-0.16
0.15
-0.13
0.17
0.50
0.54
-0.17
0.19
-0.47
4 Consistent Service
5 Manager/Team
6 Customer Involvement
7 Perf.Eval./Reporting
177 0.37 0.77 -0.79 -0.01
142 0.27 0.85 -0.14 0.57
142 1.16 0.33 -0.97 -0.16
138 0.43 0.73
0.06
0.20
1.56
0.94
-0.78
-0.15
0.05
0.31
0.40
0.89 -0.01 -0.31 -0.38 0.94
8 Personnel Eval./Comp
9
Supplier’s GAM Use-At
All
1
0
Supplier’s Performance
146 1.11
187 1.63
134 0.70
0.35
0.18
0.55
0.12
2.78*
-0.16
-0.77
-0.79
-0.36
1.48
-1.29
1.09
-0.69
-0.83
-0.88
N = 191 (includes the one respondent who did not provide supplier industry information)
0.68
3.07*
1.65
* = significant at the 1% level. a
We report the within-groups degrees of freedom ( w ) since these vary across our ten variables, depending on the number of missing data for each. The between-groups degrees of freedom are obviously 3 for all variables, and the total degrees of freedom are equal to w + 3. b
Differences are taken in the order indicated, e.g. a positive value of t in the US vs. non-US comparison indicates a larger mean for US companies. c
The tstatistics reported in this table have been computed using the pooled formula (Hays
1988). However, we also computed them in the alternative standard way, i.e. separate ttests, and obtained similar results (i.e., the only significant statistic was for the GAM use variable and the mail sample). d
Includes the mail subsample.
29

TABLE 2
Comparison of Explained Variance
Model
1
Dependent variable
Customers’ GAM
Demand
Equation
Independent variable
Customers’ Global Buying &
Supplier’s Industry
Globalization
1
Supplier’s GAM Use -
At All
Customers’ GAM Demand &
Supplier’s Industry
Globalization
2
2
2
Customers’ GAM
Demand
Supplier’s GAM Use -
Aspects
Supplier’s
Performance
Customers’ Global Buying &
Supplier’s Industry
Globalization
Customers’ GAM Demand &
Supplier’s Industry
Globalization
Supplier’s GAM Use-Aspects
R 2
0.20
0.09
0.16
0.41
0.60
30

Independent
Variable
Supplier’s
Industry
Globalization
TABLE 3
Model 1 Direct and Indirect MLE Unstandardized Estimates
Customers’ Global
Buying
Customers’ GAM
Demand
Supplier’s GAM
Use-At All
Direct
.308
(.123)
H1
*
[2.514] p < .01
Indirect
.---
Direct
.236
(.082) (.031)
[2.885]
Indirect Direct
.069
[2.207] p < .005 p< .02
H2
.031 p < .15 p< .01
H3
Indirect
.030
(.031) (.013)
[1.002] [2.366]
Customers’ Global
Buying
.223
(.050)
[4.460] p < .001
H4
.--- .--- .022
(.008)
[2.584] p < .005
Customers’ GAM
Demand
.098
(.032)
[3.095] p < .001
H5
* The first row is the unstandardized MLE estimator of the coefficient.
The second row is the estimated standard error of the coefficient.
The third row is the t ratio for the coefficient ( i.e. the ratio of the estimated coefficient to the estimated standard error).
The fourth row is the p-level for the null hypothesis of no relationship (i.e. the probability that these result would obtain when there is in fact no relationship.)
The tests are one tailed as there was a directional hypothesis (which was verified) in each case.
31

Model 2 Direct and Indirect MLE Unstandardized Estimates
Independent
Variable
Supplier’s
Industry
Globalization
Customers’
Global Buying
Customers’
Global Buying
D * I
.232 .---
(.156)
[1.485] p < .072
H1
TABLE 4
Customers’
GAM Demand
D I
.273 .044
(.106) (.033)
[2.572] [1.316] p< .005p< .010
H2
.188
(.068)
[2.751] p< .005
H4
.---
Supplier’s GAM
Use-Aspects
D I
.138 .224
(.117) (.087)
[1.177] [2.570] p < .124 p<.006
H3
.--- .133
(.054)
[2.469]
p< .007
Customers’
GAM
Demand
Supplier’s
GAM Use -
Aspects
.709 .---
(.132)
[5.380] p< .001
H5
Supplier’s
D I
.--- .277
(.104)
[2.670]
p< .005
.--- .102
(.042)
[2.440]
p< .007
.--- .543
(.106)
[5.103]
p< .001
.765 .---
(.084)
[9.139] p < .001
H6
*D indicates the direct effect of the independent variable (row variable) on the dependent variable ( column variable). I indicates the indirect effect.
The first row is the unstandardized MLE estimator of the coefficient.
The second row is the estimated standard error of the coefficient.
The third row is the t ratio for the coefficient ( i.e. the ratio of the estimated coefficient to the estimated standard error).
The fourth row is the p-level for the null hypothesis of no relationship ( i.e. the probability that the result would obtain when there is in fact no relationship).
The test are one tailed as there was a directional hypothesis (which was verified) in each case.
32

Indep. Var.
Supplier’s
Industry
Globalization
TABLE 5
Total, Direct, & Indirect Standardized Effects
T *
Customers’
Global Buying
D ** I ***
Model 1
Customers’ GAM
Demand
T D I
.196.196 -- .297.230 .067
.342 .342 .----
Supplier’s GAM
Use GAM
(dummy var.)
T D I
.158.081 .077
.
089.---- .089
.261.261
.----
Customers’
Global
Buying
Customers’
GAM
Demand
Indep. Var/
Supplier’s
Industry
Globalization
Customers’
Global Buying
T * D ** I
.155.155.---
***
Model 2
Customers’
T D
.303.261.042
GAM Demand
I
Supplier’s GAM
Use-Aspects
T D
.292.111 .181
I
Supplier’s
Performance
T D
.226.--- .226
I
Customers’
Global
Buying
Customers’
.269.269.--- .161.--- .161 .124.--- .124
GAM
Demand
Supplier’s
GAM Use -
.598.598 .--- .462.--- .462
.773.773 .---
Aspects
* Total effect of independent variable on the endogenous variable in the column.
** Direct effect of the independent variable on the endogenous variable in the column.
*** Indirect effect of the independent variable on the endogenous variable in the column
D I
33
.---

REFERENCES
Anderson, James C. and David W. Gerbing (1988): “Structural equation modeling in practice: A review and recommended two-step approach”, Psychological Bulletin , 103, 411-423.
Arnold, David, Julian Birkinshaw and Omar Toulan (2001a) “Global Account Management in
Multinational Corporations: Theory and Evidence,”
Journal of International Business Studies ,
Vol. 32, No. 2, Second Quarter 2001, pp. 231-248.
Arnold, David, Julian Birkinshaw and Omar Toulan (2001b). “Can Selling Be Globalized? The Pitfalls of Global Account Management,”
California Management Review , 44 (2), Fall, pp. 8-20.
Bartlett, Christopher A., and Sumantra Ghoshal, (1989). Managing Across Borders: The Transnational
Solution , Boston: Harvard Business School Press.
Baumgartner, Hans and Christian Homburg (1996): “Applications of structural equation modeling in marketing and consumer research: A review”,
International Journal of Research in Marketing
13, 139-161.
Bentler, Peter M. (1995): EQS Structural Equations Program Manual . Encino, CA: Multivariate
Software, Inc.
Bentler, Peter M., and Eric J.C. Wu (1995): EQS for Windows User’s Guide . Encino, CA: Multivariate
Software, Inc.
Douglas, Susan P., and C. Samuel Craig, "Evolution of Global Marketing Strategy: Scale, Scope and
Synergy," Columbia Journal of World Business , Fall 1989, pp. 47-57.
Goldberger, Arthur S. (1964), Econometric Theory: New York: Wiley.
Grönroos, Christian, “Value-Driven Relational marketing: From Products to Resources and
Competencies,”
Journal of Marketing Management , Vol. 13, No. 5, pp. 407-420.
Hays, William. L. (1988), Statistics for Psychologists: New York: Holt, Rinehart, Winston.
Jain, Subhash C. (1989): “Standardization of international marketing strategy: some research hypotheses”,
Journal of Marketing , 53, 70-79.
Jaworski, Bernard J. and Ajay K. Kohli (1993), “market Orientation: Antecedents and Consequences,”
Journal of Marketing , 57 (July), 53-70.
Johansson, Johny K. and George S. Yip (1994): “Exploiting globalization potential: U.S. and Japanese strategies”,
Strategic Management Journal , 15, 579-601.
34

Kalwani, U. Manohar, and Narakesari Narayandas (1995), “Long-Term Manufacturer-Supplier
Relationships: Do They Pay Off for Supplier Firms?” Journal of Marketing , 59 (January), 1-16.
Kim, W. Chan, and Renée Mauborgne (1995), "A Procedural Justice Model of Strategic Decision Making,"
Organization Science , Vol. 6, No. 1, January-February Edition, pp. 44-61.
Lawrence, Paul R., and Jay W. Lorsch, (1967), Organization and Environment . Boston: Graduate School of Business Administration, Harvard University.
Malnight, Thomas W., “Globalization of an Ethnocentric Firm: An Evolutionary Perspective,”
Strategic
Management Journal , Vol. 16, No. 2, February 1995, pp. 119-142.
Miles, Raymond, Charles C. Snow, and Jeffrey Pfeffer (1974), “ Organization-Environment : Concepts and Issues. ” Industrial Relations , 13 : 244-264.
Millman, Tony (1996), "Global Key Account Management and Systems Selling," International Business
Review, 5, No. 6, 631-645.
Montgomery, David B. and Yip, George S., “The Challenge of Global Customer Management,”
Marketing Management , Vol. 9, No. 4, Winter 2000, pp. 22-29.
Morgan, Robert M. and Shelby D. Hunt (1994): “The Commitment-Trust Theory of Relationship
Marketing”,
Journal of Marketing , 58, 20-38.
Morrison, Allen J., and Kendall Roth, "A Taxonomy of Business-Level Strategies in Global Industries,"
Strategic Management Journal , Vol. 13, 1992, pp. 319-418.
Nahapiet, Janine (1994): “Servicing the global client: Towards global account management?”, paper presented at the 14th Annual Conference of the Strategic Management Society, Groupe HEC,
Jouy-en-Josas, France, September.
Nahapiet, Janine, (1998), “Strategies for the global service firm”,
Financial Times , February 6.
Nunnally, J. C. (1978), Introduction to Statistics for Psychology and Education . New York, NY:
McGraw-Hill,
Parvatiyar, Atul, and Thomas Gruen, (1999), “ Global Account Management Effectiveness : A
Contingency Model ”, working paper, Goizueta School of Business, Emory University, Atlanta,
GA.
Porter, Michael E. (1986): “Changing patterns of international competition”,
California Management
Review , 28 (2), 9-40.
Quelch, John A., and Edward J. Hoff, "Customizing global marketing," Harvard Business Review ,
May-June 1986, pp. 59-68.
35

Roth, Kendall, David Schweiger and Allen J. Morrison, (1991). "Global Strategy Implementation at the
Business Unit Level: Operational Capabilities and Administrative Mechanisms," Journal of
International Business Studies , 22(3), 369-407.
Rumelt, Richard P. (1991). “How much does industry matter?”
Strategic Management Journal, Vol. 12,
No. 3, pp. 167-185.
Yip, George S. (1992), Total Global Strategy : Upper Saddle River, NJ: Prentice-Hall.
Yip, George S., and Tammy L. Madsen (1996): “Global account management: The new frontier in relationship marketing”,
International Marketing Review , 13 (3), 24-42.
36

NOTES
1
Since alphas based on correlation and covariance matrices in general differ (though only slightly in our case) we conservatively report here the covariance matrix-based ones, which represent a lower bound to the internal consistency of the raw-score sum (Bentler, 1995).
2 The standardized coefficients reflect the relative importance of the independent variables to each dependent variable. In effect, the standardized coefficients may be viewed as the typical change or variation in a dependent variable induced by or associated with a typical variation in the independent variable (Goldberger, 1964).
3
The relationship between the standardized and the unstandardized estimates is that the standardized estimates equal the unstandardized estimate times the ratio of the standard deviation of the independent variable to the standard deviation of the dependent variable in the sample.
37